Utilizing data object storage tracking in a dispersed storage network

ABSTRACT

A method begins by a dispersed storage (DS) processing module dividing a very large data object into a plurality of data regions and generating a data object storage tracking table that includes sections for identifying one or more data regions that are available or unavailable for retrieval. The method continues with the DS processing module dividing a first data region into data segments and disperse storage error encoding the data segments to produce sets of encoded data slices. The method continues with the DS processing module sending DSN write requests regarding storing the sets of encoded data slices to storage units and when at least a write threshold number of write responses is received for each of the sets of encoded data slices, updating the data object storage tracking table to indicate that the first data region is available for retrieval.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to 35 U.S.C. §119(e) to the following U.S. Provisional patent application which is hereby incorporated herein by reference in its entirety and made part of the present U.S. Utility patent application for all purposes:

1. U.S. Provisional Application Ser. No. 61/733,698, entitled “ACCESSING DATA UTILIZING A REGION HEADER OBJECT,” , filed Dec. 5, 2012, pending.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and more particularly to dispersed storage of data and distributed task processing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/or store data. Such computing devices range from wireless smart phones, laptops, tablets, personal computers (PC), work stations, and video game devices, to data centers that support millions of web searches, stock trades, or on-line purchases every day. In general, a computing device includes a central processing unit (CPU), a memory system, user input/output interfaces, peripheral device interfaces, and an interconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using “cloud computing” to perform one or more computing functions (e.g., a service, an application, an algorithm, an arithmetic logic function, etc.) on behalf of the computer. Further, for large services, applications, and/or functions, cloud computing may be performed by multiple cloud computing resources in a distributed manner to improve the response time for completion of the service, application, and/or function. For example, Hadoop is an open source software framework that supports distributed applications enabling application execution by thousands of computers.

In addition to cloud computing, a computer may use “cloud storage” as part of its memory system. As is known, cloud storage enables a user, via its computer, to store files, applications, etc. on an Internet storage system. The Internet storage system may include a RAID (redundant array of independent disks) system and/or a dispersed storage system that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributed computing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing core in accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and task processing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing in accordance with the present invention;

FIG. 5 is a logic diagram of an example of a method for outbound DST processing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersed error encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of the dispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicing processing of the dispersed error encoding in accordance with the present invention;

FIG. 9 is a diagram of an example of grouping selection processing of the outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groups in accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST execution unit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DST execution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbound distributed storage and/or task (DST) processing in accordance with the present invention;

FIG. 14 is a logic diagram of an example of a method for inbound DST processing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processing of the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersed error decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decoding processing of the dispersed error decoding in accordance with the present invention;

FIG. 18 is a diagram of an example of a de-segment processing of the dispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into data in accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within the distributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation of outbound distributed storage and/or task (DST) processing for storing data in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed error encoding for the example of FIG. 21 in accordance with the present invention;

FIG. 23 is a diagram of an example of converting data into pillar slice groups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storage operation of a DST execution unit in accordance with the present invention;

FIG. 25 is a schematic block diagram of an example of operation of inbound distributed storage and/or task (DST) processing for retrieving dispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed error decoding for the example of FIG. 25 in accordance with the present invention;

FIG. 27 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing a plurality of data and a plurality of task codes in accordance with the present invention;

FIG. 28 is a schematic block diagram of an example of the distributed computing system performing tasks on stored data in accordance with the present invention;

FIG. 29 is a schematic block diagram of an embodiment of a task distribution module facilitating the example of FIG. 28 in accordance with the present invention;

FIG. 30 is a diagram of a specific example of the distributed computing system performing tasks on stored data in accordance with the present invention;

FIG. 31 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing data and task codes for the example of FIG. 30 in accordance with the present invention;

FIG. 32 is a diagram of an example of DST allocation information for the example of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performing the example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information into final results for the example of FIG. 30 in accordance with the present invention;

FIG. 40A is a diagram of an embodiment of a structure of a large data object in accordance with the present invention;

FIG. 40B is a diagram of an embodiment of a structure of a data object storage tracking table in accordance with the present invention;

FIG. 40C is a schematic block diagram of an embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 40D is a diagram of an example of writing data regions in accordance with the present invention;

FIG. 40E is a diagram of an example of writing a data region in accordance with the present invention;

FIGS. 40F-J are diagrams of an embodiment of a dispersed storage network (DSN) illustrating steps of an example of writing data in accordance with the present invention;

FIG. 41 is a flowchart illustrating an example of reading a data object in accordance with the present invention;

FIG. 42 is a flowchart illustrating an example of writing a data object in accordance with the present invention;

FIG. 43 is a flowchart illustrating an example of deleting a data object in accordance with the present invention;

FIG. 44 is a flowchart illustrating an example of overwriting a data object in accordance with the present invention;

FIG. 45 is a flowchart illustrating an example of resulting storage conflicts in accordance with the present invention;

FIG. 46A is a schematic block diagram of an embodiment of a dispersed storage system in accordance with the present invention;

FIG. 46B is a flowchart illustrating an example of storing data in accordance with the present invention;

FIG. 47A is a schematic block diagram of another embodiment of a dispersed storage system in accordance with the present invention; and

FIG. 47B is a flowchart illustrating another example of storing data in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributed computing system 10 that includes a user device 12 and/or a user device 14, a distributed storage and/or task (DST) processing unit 16, a distributed storage and/or task network (DSTN) managing unit 18, a DST integrity processing unit 20, and a distributed storage and/or task network (DSTN) module 22. The components of the distributed computing system 10 are coupled via a network 24, which may include one or more wireless and/or wire lined communication systems; one or more private intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/or task (DST) execution units 36 that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.). Each of the DST execution units is operable to store dispersed error encoded data and/or to execute, in a distributed manner, one or more tasks on data. The tasks may be a simple function (e.g., a mathematical function, a logic function, an identify function, a find function, a search engine function, a replace function, etc.), a complex function (e.g., compression, human and/or computer language translation, text-to-voice conversion, voice-to-text conversion, etc.), multiple simple and/or complex functions, one or more algorithms, one or more applications, etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTN managing unit 18, and the DST integrity processing unit 20 include a computing core 26 and may be a portable computing device and/or a fixed computing device. A portable computing device may be a social networking device, a gaming device, a cell phone, a smart phone, a personal digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, and/or any other portable device that includes a computing core. A fixed computing device may be a personal computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a video game console, and/or any type of home or office computing equipment. User device 12 and DST processing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includes software and/or hardware to support one or more communication links via the network 24 indirectly and/or directly. For example, interface 30 supports a communication link (e.g., wired, wireless, direct, via a LAN, via the network 24, etc.) between user device 14 and the DST processing unit 16. As another example, interface 32 supports communication links (e.g., a wired connection, a wireless connection, a LAN connection, and/or any other type of connection to/from the network 24) between user device 12 and the DSTN module 22 and between the DST processing unit 16 and the DSTN module 22. As yet another example, interface 33 supports a communication link for each of the DSTN managing unit 18 and DST integrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersed storage (DS) error encoded data storage and retrieval, to support distributed task processing on received data, and/or to support distributed task processing on stored data. In general and with respect to DS error encoded data storage and retrieval, the distributed computing system 10 supports three primary operations: storage management, data storage and retrieval (an example of which will be discussed with reference to FIGS. 20-26), and data storage integrity verification. In accordance with these three primary functions, data can be encoded, distributedly stored in physically different locations, and subsequently retrieved in a reliable and secure manner. Such a system is tolerant of a significant number of failures (e.g., up to a failure level, which may be greater than or equal to a pillar width minus a decode threshold minus one) that may result from individual storage device failures and/or network equipment failures without loss of data and without the need for a redundant or backup copy. Further, the system allows the data to be stored for an indefinite period of time without data loss and does so in a secure manner (e.g., the system is very resistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage and retrieval) begins and ends with a user device 12-14. For instance, if a second type of user device 14 has data 40 to store in the DSTN module 22, it sends the data 40 to the DST processing unit 16 via its interface 30. The interface 30 functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). In addition, the interface 30 may attach a user identification code (ID) to the data 40.

To support storage management, the DSTN managing unit 18 performs DS management services. One such DS management service includes the DSTN managing unit 18 establishing distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for a user device 12-14 individually or as part of a group of user devices. For example, the DSTN managing unit 18 coordinates creation of a vault (e.g., a virtual memory block) within memory of the DSTN module 22 for a user device, a group of devices, or for public access and establishes per vault dispersed storage (DS) error encoding parameters for a vault. The DSTN managing unit 18 may facilitate storage of DS error encoding parameters for each vault of a plurality of vaults by updating registry information for the distributed computing system 10. The facilitating includes storing updated registry information in one or more of the DSTN module 22, the user device 12, the DST processing unit 16, and the DST integrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage error coding parameters) include data segmenting information (e.g., how many segments data (e.g., a file, a group of files, a data block, etc.) is divided into), segment security information (e.g., per segment encryption, compression, integrity checksum, etc.), error coding information (e.g., pillar width, decode threshold, read threshold, write threshold, etc.), slicing information (e.g., the number of encoded data slices that will be created for each data segment); and slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information (e.g., an access control list (ACL)) in local memory and/or within memory of the DSTN module 22. The user profile information includes authentication information, permissions, and/or the security parameters. The security parameters may include encryption/decryption scheme, one or more encryption keys, key generation scheme, and/or data encoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, the DSTN managing unit 18 tracks the number of times a user accesses a private vault and/or public vaults, which can be used to generate a per-access billing information. In another instance, the DSTN managing unit 18 tracks the amount of data stored and/or retrieved by a user device and/or a user group, which can be used to generate a per-data-amount billing information.

Another DS management service includes the DSTN managing unit 18 performing network operations, network administration, and/or network maintenance. Network operations includes authenticating user data allocation requests (e.g., read and/or write requests), managing creation of vaults, establishing authentication credentials for user devices, adding/deleting components (e.g., user devices, DST execution units, and/or DST processing units) from the distributed computing system 10, and/or establishing authentication credentials for DST execution units 36. Network administration includes monitoring devices and/or units for failures, maintaining vault information, determining device and/or unit activation status, determining device and/or unit loading, and/or determining any other system level operation that affects the performance level of the system 10. Network maintenance includes facilitating replacing, upgrading, repairing, and/or expanding a device and/or unit of the system 10.

To support data storage integrity verification within the distributed computing system 10, the DST integrity processing unit 20 performs rebuilding of ‘bad’ or missing encoded data slices. At a high level, the DST integrity processing unit 20 performs rebuilding by periodically attempting to retrieve/list encoded data slices, and/or slice names of the encoded data slices, from the DSTN module 22. For retrieved encoded slices, they are checked for errors due to data corruption, outdated version, etc. If a slice includes an error, it is flagged as a ‘bad’ slice. For encoded data slices that were not received and/or not listed, they are flagged as missing slices. Bad and/or missing slices are subsequently rebuilt using other retrieved encoded data slices that are deemed to be good slices to produce rebuilt slices. The rebuilt slices are stored in memory of the DSTN module 22. Note that the DST integrity processing unit 20 may be a separate unit as shown, it may be included in the DSTN module 22, it may be included in the DST processing unit 16, and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributed computing system 10 has two primary operations: DST (distributed storage and/or task processing) management and DST execution on received data (an example of which will be discussed with reference to FIGS. 3-19). With respect to the storage portion of the DST management, the DSTN managing unit 18 functions as previously described. With respect to the tasking processing of the DST management, the DSTN managing unit 18 performs distributed task processing (DTP) management services. One such DTP management service includes the DSTN managing unit 18 establishing DTP parameters (e.g., user-vault affiliation information, billing information, user-task information, etc.) for a user device 12-14 individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18 performing DTP network operations, network administration (which is essentially the same as described above), and/or network maintenance (which is essentially the same as described above). Network operations include, but are not limited to, authenticating user task processing requests (e.g., valid request, valid user, etc.), authenticating results and/or partial results, establishing DTP authentication credentials for user devices, adding/deleting components (e.g., user devices, DST execution units, and/or DST processing units) from the distributed computing system, and/or establishing DTP authentication credentials for DST execution units.

To support distributed task processing on stored data, the distributed computing system 10 has two primary operations: DST (distributed storage and/or task) management and DST execution on stored data. With respect to the DST execution on stored data, if the second type of user device 14 has a task request 38 for execution by the DSTN module 22, it sends the task request 38 to the DST processing unit 16 via its interface 30. An example of DST execution on stored data will be discussed in greater detail with reference to FIGS. 27-39. With respect to the DST management, it is substantially similar to the DST management to support distributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core 26 that includes a processing module 50, a memory controller 52, main memory 54, a video graphics processing unit 55, an input/output (TO) controller 56, a peripheral component interconnect (PCI) interface 58, an IO interface module 60, at least one IO device interface module 62, a read only memory (ROM) basic input output system (BIOS) 64, and one or more memory interface modules. The one or more memory interface module(s) includes one or more of a universal serial bus (USB) interface module 66, a host bus adapter (HBA) interface module 68, a network interface module 70, a flash interface module 72, a hard drive interface module 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). The DSTN interface module 76 and/or the network interface module 70 may function as the interface 30 of the user device 14 of FIG. 1. Further note that the IO device interface module 62 and/or the memory interface modules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing system performing a distributed storage and task processing operation. The distributed computing system includes a DST (distributed storage and/or task) client module 34 (which may be in user device 14 and/or in DST processing unit 16 of FIG. 1), a network 24, a plurality of DST execution units 1-n that includes two or more DST execution units 36 of FIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), a DST managing module (not shown), and a DST integrity verification module (not shown). The DST client module 34 includes an outbound DST processing section 80 and an inbound DST processing section 82. Each of the DST execution units 1-n includes a controller 86, a processing module 84, memory 88, a DT (distributed task) execution module 90, and a DST client module 34.

In an example of operation, the DST client module 34 receives data 92 and one or more tasks 94 to be performed upon the data 92. The data 92 may be of any size and of any content, where, due to the size (e.g., greater than a few Terabytes), the content (e.g., secure data, etc.), and/or task(s) (e.g., MIPS intensive), distributed processing of the task(s) on the data is desired. For example, the data 92 may be one or more digital books, a copy of a company's emails, a large-scale Internet search, a video security file, one or more entertainment video files (e.g., television programs, movies, etc.), data files, and/or any other large amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80 receives the data 92 and the task(s) 94. The outbound DST processing section 80 processes the data 92 to produce slice groupings 96. As an example of such processing, the outbound DST processing section 80 partitions the data 92 into a plurality of data partitions. For each data partition, the outbound DST processing section 80 dispersed storage (DS) error encodes the data partition to produce encoded data slices and groups the encoded data slices into a slice grouping 96. In addition, the outbound DST processing section 80 partitions the task 94 into partial tasks 98, where the number of partial tasks 98 may correspond to the number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24, the slice groupings 96 and the partial tasks 98 to the DST execution units 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DST processing section 80 sends slice group 1 and partial task 1 to DST execution unit 1. As another example, the outbound DST processing section 80 sends slice group #n and partial task #n to DST execution unit #n.

Each DST execution unit performs its partial task 98 upon its slice group 96 to produce partial results 102. For example, DST execution unit #1 performs partial task #1 on slice group #1 to produce a partial result #1, for results. As a more specific example, slice group #1 corresponds to a data partition of a series of digital books and the partial task #1 corresponds to searching for specific phrases, recording where the phrase is found, and establishing a phrase count. In this more specific example, the partial result #1 includes information as to where the phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, the DST execution units send, via the network 24, their partial results 102 to the inbound DST processing section 82 of the DST client module 34. The inbound DST processing section 82 processes the received partial results 102 to produce a result 104. Continuing with the specific example of the preceding paragraph, the inbound DST processing section 82 combines the phrase count from each of the DST execution units 36 to produce a total phrase count. In addition, the inbound DST processing section 82 combines the ‘where the phrase was found’ information from each of the DST execution units 36 within their respective data partitions to produce ‘where the phrase was found’ information for the series of digital books.

In another example of operation, the DST client module 34 requests retrieval of stored data within the memory of the DST execution units 36 (e.g., memory of the DSTN module). In this example, the task 94 is retrieve data stored in the memory of the DSTN module. Accordingly, the outbound DST processing section 80 converts the task 94 into a plurality of partial tasks 98 and sends the partial tasks 98 to the respective DST execution units 1-n.

In response to the partial task 98 of retrieving stored data, a DST execution unit 36 identifies the corresponding encoded data slices 100 and retrieves them. For example, DST execution unit #1 receives partial task #1 and retrieves, in response thereto, retrieved slices #1. The DST execution units 36 send their respective retrieved slices 100 to the inbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100 into data 92. For example, the inbound DST processing section 82 de-groups the retrieved slices 100 to produce encoded slices per data partition. The inbound DST processing section 82 then DS error decodes the encoded slices per data partition to produce data partitions. The inbound DST processing section 82 de-partitions the data partitions to recapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing section 80 of a DST client module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., a plurality of n DST execution units 36) via a network 24. The outbound DST processing section 80 includes a data partitioning module 110, a dispersed storage (DS) error encoding module 112, a grouping selector module 114, a control module 116, and a distributed task control module 118.

In an example of operation, the data partitioning module 110 partitions data 92 into a plurality of data partitions 120. The number of partitions and the size of the partitions may be selected by the control module 116 via control 160 based on the data 92 (e.g., its size, its content, etc.), a corresponding task 94 to be performed (e.g., simple, complex, single step, multiple steps, etc.), DS encoding parameters (e.g., pillar width, decode threshold, write threshold, segment security parameters, slice security parameters, etc.), capabilities of the DST execution units 36 (e.g., processing resources, availability of processing recourses, etc.), and/or as may be inputted by a user, system administrator, or other operator (human or automated). For example, the data partitioning module 110 partitions the data 92 (e.g., 100 Terabytes) into 100,000 data segments, each being 1 Gigabyte in size. Alternatively, the data partitioning module 110 partitions the data 92 into a plurality of data segments, where some of data segments are of a different size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in a serial manner, a parallel manner, and/or a combination thereof. For each data partition 120, the DS error encoding module 112 DS error encodes the data partition 120 in accordance with control information 160 from the control module 116 to produce encoded data slices 122. The DS error encoding includes segmenting the data partition into data segments, segment security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC), etc.), error encoding, slicing, and/or per slice security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC), etc.). The control information 160 indicates which steps of the DS error encoding are active for a given data partition and, for active steps, indicates the parameters for the step. For example, the control information 160 indicates that the error encoding is active and includes error encoding parameters (e.g., pillar width, decode threshold, write threshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a data partition into a set of slice groupings 96. The number of slice groupings corresponds to the number of DST execution units 36 identified for a particular task 94. For example, if five DST execution units 36 are identified for the particular task 94, the grouping selector module groups the encoded slices 122 of a data partition into five slice groupings 96. The grouping selector module 114 outputs the slice groupings 96 to the corresponding DST execution units 36 via the network 24.

The distributed task control module 118 receives the task 94 and converts the task 94 into a set of partial tasks 98. For example, the distributed task control module 118 receives a task to find where in the data (e.g., a series of books) a phrase occurs and a total count of the phrase usage in the data. In this example, the distributed task control module 118 replicates the task 94 for each DST execution unit 36 to produce the partial tasks 98. In another example, the distributed task control module 118 receives a task to find where in the data a first phrase occurs, where in the data a second phrase occurs, and a total count for each phrase usage in the data. In this example, the distributed task control module 118 generates a first set of partial tasks 98 for finding and counting the first phrase and a second set of partial tasks for finding and counting the second phrase. The distributed task control module 118 sends respective first and/or second partial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbound distributed storage and task (DST) processing that begins at step 126 where a DST client module receives data and one or more corresponding tasks. The method continues at step 128 where the DST client module determines a number of DST units to support the task for one or more data partitions. For example, the DST client module may determine the number of DST units to support the task based on the size of the data, the requested task, the content of the data, a predetermined number (e.g., user indicated, system administrator determined, etc.), available DST units, capability of the DST units, and/or any other factor regarding distributed task processing of the data. The DST client module may select the same DST units for each data partition, may select different DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determines processing parameters of the data based on the number of DST units selected for distributed task processing. The processing parameters include data partitioning information, DS encoding parameters, and/or slice grouping information. The data partitioning information includes a number of data partitions, size of each data partition, and/or organization of the data partitions (e.g., number of data blocks in a partition, the size of the data blocks, and arrangement of the data blocks). The DS encoding parameters include segmenting information, segment security information, error encoding information (e.g., dispersed storage error encoding function parameters including one or more of pillar width, decode threshold, write threshold, read threshold, generator matrix), slicing information, and/or per slice security information. The slice grouping information includes information regarding how to arrange the encoded data slices into groups for the selected DST units. As a specific example, if the DST client module determines that five DST units are needed to support the task, then it determines that the error encoding parameters include a pillar width of five and a decode threshold of three.

The method continues at step 132 where the DST client module determines task partitioning information (e.g., how to partition the tasks) based on the selected DST units and data processing parameters. The data processing parameters include the processing parameters and DST unit capability information. The DST unit capability information includes the number of DT (distributed task) execution units, execution capabilities of each DT execution unit (e.g., MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, memory information (e.g., type, size, availability, etc.), and/or any information germane to executing one or more tasks.

The method continues at step 134 where the DST client module processes the data in accordance with the processing parameters to produce slice groupings. The method continues at step 136 where the DST client module partitions the task based on the task partitioning information to produce a set of partial tasks. The method continues at step 138 where the DST client module sends the slice groupings and the corresponding partial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersed storage (DS) error encoding module 112 of an outbound distributed storage and task (DST) processing section. The DS error encoding module 112 includes a segment processing module 142, a segment security processing module 144, an error encoding module 146, a slicing module 148, and a per slice security processing module 150. Each of these modules is coupled to a control module 116 to receive control information 160 therefrom.

In an example of operation, the segment processing module 142 receives a data partition 120 from a data partitioning module and receives segmenting information as the control information 160 from the control module 116. The segmenting information indicates how the segment processing module 142 is to segment the data partition 120. For example, the segmenting information indicates how many rows to segment the data based on a decode threshold of an error encoding scheme, indicates how many columns to segment the data into based on a number and size of data blocks within the data partition 120, and indicates how many columns to include in a data segment 152. The segment processing module 142 segments the data 120 into data segments 152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the control module 116, secures the data segments 152 based on segment security information received as control information 160 from the control module 116. The segment security information includes data compression, encryption, watermarking, integrity check (e.g., cyclic redundancy check (CRC), etc.), and/or any other type of digital security. For example, when the segment security processing module 144 is enabled, it may compress a data segment 152, encrypt the compressed data segment, and generate a CRC value for the encrypted data segment to produce a secure data segment 154. When the segment security processing module 144 is not enabled, it passes the data segments 152 to the error encoding module 146 or is bypassed such that the data segments 152 are provided to the error encoding module 146.

The error encoding module 146 encodes the secure data segments 154 in accordance with error correction encoding parameters received as control information 160 from the control module 116. The error correction encoding parameters (e.g., also referred to as dispersed storage error coding parameters) include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an online coding algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction encoding parameters identify a specific error correction encoding scheme, specifies a pillar width of five, and specifies a decode threshold of three. From these parameters, the error encoding module 146 encodes a data segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordance with the pillar width of the error correction encoding parameters received as control information 160. For example, if the pillar width is five, the slicing module 148 slices an encoded data segment 156 into a set of five encoded data slices. As such, for a plurality of encoded data segments 156 for a given data partition, the slicing module outputs a plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by the control module 116, secures each encoded data slice 158 based on slice security information received as control information 160 from the control module 116. The slice security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the per slice security processing module 150 is enabled, it compresses an encoded data slice 158, encrypts the compressed encoded data slice, and generates a CRC value for the encrypted encoded data slice to produce a secure encoded data slice 122. When the per slice security processing module 150 is not enabled, it passes the encoded data slices 158 or is bypassed such that the encoded data slices 158 are the output of the DS error encoding module 112. Note that the control module 116 may be omitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersed storage (DS) error encoding module. In this example, a segment processing module 142 receives a data partition 120 that includes 45 data blocks (e.g., d1-d45), receives segmenting information (i.e., control information 160) from a control module, and segments the data partition 120 in accordance with the control information 160 to produce data segments 152. Each data block may be of the same size as other data blocks or of a different size. In addition, the size of each data block may be a few bytes to megabytes of data. As previously mentioned, the segmenting information indicates how many rows to segment the data partition into, indicates how many columns to segment the data partition into, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme is three; as such the number of rows to divide the data partition into is three. The number of columns for each row is set to 15, which is based on the number and size of data blocks. The data blocks of the data partition are arranged in rows and columns in a sequential order (i.e., the first row includes the first 15 data blocks; the second row includes the second 15 data blocks; and the third row includes the last 15 data blocks).

With the data blocks arranged into the desired sequential order, they are divided into data segments based on the segmenting information. In this example, the data partition is divided into 8 data segments; the first 7 include 2 columns of three rows and the last includes 1 column of three rows. Note that the first row of the 8 data segments is in sequential order of the first 15 data blocks; the second row of the 8 data segments in sequential order of the second 15 data blocks; and the third row of the 8 data segments in sequential order of the last 15 data blocks. Note that the number of data blocks, the grouping of the data blocks into segments, and size of the data blocks may vary to accommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicing processing of the dispersed error encoding processing the data segments of FIG. 7. In this example, data segment 1 includes 3 rows with each row being treated as one word for encoding. As such, data segment 1 includes three words for encoding: word 1 including data blocks d1 and d2, word 2 including data blocks d16 and d17, and word 3 including data blocks d31 and d32. Each of data segments 2-7 includes three words where each word includes two data blocks. Data segment 8 includes three words where each word includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148 convert each data segment into a set of encoded data slices in accordance with error correction encoding parameters as control information 160. More specifically, when the error correction encoding parameters indicate a unity matrix Reed-Solomon based encoding algorithm, 5 pillars, and decode threshold of 3, the first three encoded data slices of the set of encoded data slices for a data segment are substantially similar to the corresponding word of the data segment. For instance, when the unity matrix Reed-Solomon based encoding algorithm is applied to data segment 1, the content of the first encoded data slice (DS1_d1&2) of the first set of encoded data slices (e.g., corresponding to data segment 1) is substantially similar to content of the first word (e.g., d1 & d2); the content of the second encoded data slice (DS1_d16&17) of the first set of encoded data slices is substantially similar to content of the second word (e.g., d16 & d17); and the content of the third encoded data slice (DS1_d31&32) of the first set of encoded data slices is substantially similar to content of the third word (e.g., d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 and ES1_2) of the first set of encoded data slices include error correction data based on the first—third words of the first data segment. With such an encoding and slicing scheme, retrieving any three of the five encoded data slices allows the data segment to be accurately reconstructed.

The encoding and slicing of data segments 2-7 yield sets of encoded data slices similar to the set of encoded data slices of data segment 1. For instance, the content of the first encoded data slice (DS2_d3&4) of the second set of encoded data slices (e.g., corresponding to data segment 2) is substantially similar to content of the first word (e.g., d3 & d4); the content of the second encoded data slice (DS2_d18&19) of the second set of encoded data slices is substantially similar to content of the second word (e.g., d18 & d19); and the content of the third encoded data slice (DS2_d33&34) of the second set of encoded data slices is substantially similar to content of the third word (e.g., d33 & d34). The content of the fourth and fifth encoded data slices (e.g., ES1_1 and ES1_2) of the second set of encoded data slices includes error correction data based on the first—third words of the second data segment.

FIG. 9 is a diagram of an example of grouping selection processing of an outbound distributed storage and task (DST) processing in accordance with group selection information as control information 160 from a control module. Encoded slices for data partition 122 are grouped in accordance with the control information 160 to produce slice groupings 96. In this example, a grouping selector module 114 organizes the encoded data slices into five slice groupings (e.g., one for each DST execution unit of a distributed storage and task network (DSTN) module). As a specific example, the grouping selector module 114 creates a first slice grouping for a DST execution unit #1, which includes first encoded slices of each of the sets of encoded slices. As such, the first DST execution unit receives encoded data slices corresponding to data blocks 1-15 (e.g., encoded data slices of contiguous data).

The grouping selector module 114 also creates a second slice grouping for a DST execution unit #2, which includes second encoded slices of each of the sets of encoded slices. As such, the second DST execution unit receives encoded data slices corresponding to data blocks 16-30. The grouping selector module 114 further creates a third slice grouping for DST execution unit #3, which includes third encoded slices of each of the sets of encoded slices. As such, the third DST execution unit receives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DST execution unit #4, which includes fourth encoded slices of each of the sets of encoded slices. As such, the fourth DST execution unit receives encoded data slices corresponding to first error encoding information (e.g., encoded data slices of error coding (EC) data). The grouping selector module 114 further creates a fifth slice grouping for DST execution unit #5, which includes fifth encoded slices of each of the sets of encoded slices. As such, the fifth DST execution unit receives encoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slice groups that expands on the preceding figures. As shown, the data 92 is partitioned in accordance with a partitioning function 164 into a plurality of data partitions (1-x, where x is an integer greater than 4). Each data partition (or chunkset of data) is encoded and grouped into slice groupings as previously discussed by an encoding and grouping function 166. For a given data partition, the slice groupings are sent to distributed storage and task (DST) execution units. From data partition to data partition, the ordering of the slice groupings to the DST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DST execution units such that the first DST execution receives first encoded data slices of each of the sets of encoded data slices, which corresponds to a first continuous data chunk of the first data partition (e.g., refer to FIG. 9), a second DST execution receives second encoded data slices of each of the sets of encoded data slices, which corresponds to a second continuous data chunk of the first data partition, etc.

For the second data partition, the slice groupings may be sent to the DST execution units in a different order than it was done for the first data partition. For instance, the first slice grouping of the second data partition (e.g., slice group 2_1) is sent to the second DST execution unit; the second slice grouping of the second data partition (e.g., slice group 2_2) is sent to the third DST execution unit; the third slice grouping of the second data partition (e.g., slice group 2_3) is sent to the fourth DST execution unit; the fourth slice grouping of the second data partition (e.g., slice group 2_4, which includes first error coding information) is sent to the fifth DST execution unit; and the fifth slice grouping of the second data partition (e.g., slice group 2_5, which includes second error coding information) is sent to the first DST execution unit.

The pattern of sending the slice groupings to the set of DST execution units may vary in a predicted pattern, a random pattern, and/or a combination thereof from data partition to data partition. In addition, from data partition to data partition, the set of DST execution units may change. For example, for the first data partition, DST execution units 1-5 may be used; for the second data partition, DST execution units 6-10 may be used; for the third data partition, DST execution units 3-7 may be used; etc. As is also shown, the task is divided into partial tasks that are sent to the DST execution units in conjunction with the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST (distributed storage and/or task) execution unit that includes an interface 169, a controller 86, memory 88, one or more DT (distributed task) execution modules 90, and a DST client module 34. The memory 88 is of sufficient size to store a significant number of encoded data slices (e.g., thousands of slices to hundreds-of-millions of slices) and may include one or more hard drives and/or one or more solid-state memory devices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution module receives a slice grouping 96 (e.g., slice group #1) via interface 169. The slice grouping 96 includes, per partition, encoded data slices of contiguous data or encoded data slices of error coding (EC) data. For slice group #1 , the DST execution module receives encoded data slices of contiguous data for partitions #1 and #x (and potentially others between 3 and x) and receives encoded data slices of EC data for partitions #2 and #3 (and potentially others between 3 and x). Examples of encoded data slices of contiguous data and encoded data slices of error coding (EC) data are discussed with reference to FIG. 9. The memory 88 stores the encoded data slices of slice groupings 96 in accordance with memory control information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates the memory control information 174 based on a partial task(s) 98 and distributed computing information (e.g., user information (e.g., user ID, distributed computing permissions, data access permission, etc.), vault information (e.g., virtual memory assigned to user, user group, temporary storage for task processing, etc.), task validation information, etc.). For example, the controller 86 interprets the partial task(s) 98 in light of the distributed computing information to determine whether a requestor is authorized to perform the task 98, is authorized to access the data, and/or is authorized to perform the task on this particular data. When the requestor is authorized, the controller 86 determines, based on the task 98 and/or another input, whether the encoded data slices of the slice grouping 96 are to be temporarily stored or permanently stored. Based on the foregoing, the controller 86 generates the memory control information 174 to write the encoded data slices of the slice grouping 96 into the memory 88 and to indicate whether the slice grouping 96 is permanently stored or temporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86 facilitates execution of the partial task(s) 98. In an example, the controller 86 interprets the partial task 98 in light of the capabilities of the DT execution module(s) 90. The capabilities include one or more of MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, etc. If the controller 86 determines that the DT execution module(s) 90 have sufficient capabilities, it generates task control information 176.

The task control information 176 may be a generic instruction (e.g., perform the task on the stored slice grouping) or a series of operational codes. In the former instance, the DT execution module 90 includes a co-processor function specifically configured (fixed or programmed) to perform the desired task 98. In the latter instance, the DT execution module 90 includes a general processor topology where the controller stores an algorithm corresponding to the particular task 98. In this instance, the controller 86 provides the operational codes (e.g., assembly language, source code of a programming language, object code, etc.) of the algorithm to the DT execution module 90 for execution.

Depending on the nature of the task 98, the DT execution module 90 may generate intermediate partial results 102 that are stored in the memory 88 or in a cache memory (not shown) within the DT execution module 90. In either case, when the DT execution module 90 completes execution of the partial task 98, it outputs one or more partial results 102. The partial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of the DT execution module(s) 90 can support the partial task 98, the controller 86 determines that the DT execution module(s) 90 cannot adequately support the task 98 (e.g., does not have the right resources, does not have sufficient available resources, available resources would be too slow, etc.), it then determines whether the partial task 98 should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fully offloaded, it generates DST control information 178 and provides it to the DST client module 34. The DST control information 178 includes the partial task 98, memory storage information regarding the slice grouping 96, and distribution instructions. The distribution instructions instruct the DST client module 34 to divide the partial task 98 into sub-partial tasks 172, to divide the slice grouping 96 into sub-slice groupings 170, and identify other DST execution units. The DST client module 34 functions in a similar manner as the DST client module 34 of FIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slice groupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partial results), via the interface 169, from the DST execution units to which the task was offloaded. The DST client module 34 provides the sub-partial results to the DST execution unit, which processes the sub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should be partially offloaded, it determines what portion of the task 98 and/or slice grouping 96 should be processed locally and what should be offloaded. For the portion that is being locally processed, the controller 86 generates task control information 176 as previously discussed. For the portion that is being offloaded, the controller 86 generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g., sub-partial results) from the DST executions units to which a portion of the task was offloaded, it provides the sub-partial results to the DT execution module 90. The DT execution module 90 processes the sub-partial results with the sub-partial results it created to produce the partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of stored slices 100, stored results 104, partial results 102 when the DT execution module 90 stores partial results 102 and/or results 104 in the memory 88. For example, when the partial task 98 includes a retrieval request, the controller 86 outputs the memory control 174 to the memory 88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of a distributed storage and task (DST) execution unit storing encoded data slices and executing a task thereon. To store the encoded data slices of a partition 1 of slice grouping 1, a controller 86 generates write commands as memory control information 174 such that the encoded slices are stored in desired locations (e.g., permanent or temporary) within memory 88.

Once the encoded slices are stored, the controller 86 provides task control information 176 to a distributed task (DT) execution module 90. As a first step of executing the task in accordance with the task control information 176, the DT execution module 90 retrieves the encoded slices from memory 88. The DT execution module 90 then reconstructs contiguous data blocks of a data partition. As shown for this example, reconstructed contiguous data blocks of data partition 1 include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module 90 performs the task on the reconstructed contiguous data blocks. For example, the task may be to search the reconstructed contiguous data blocks for a particular word or phrase, identify where in the reconstructed contiguous data blocks the particular word or phrase occurred, and/or count the occurrences of the particular word or phrase on the reconstructed contiguous data blocks. The DST execution unit continues in a similar manner for the encoded data slices of other partitions in slice grouping 1. Note that with using the unity matrix error encoding scheme previously discussed, if the encoded data slices of contiguous data are uncorrupted, the decoding of them is a relatively straightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (or missing), it can be rebuilt by accessing other DST execution units that are storing the other encoded data slices of the set of encoded data slices of the corrupted encoded data slice. In this instance, the DST execution unit having the corrupted encoded data slices retrieves at least three encoded data slices (of contiguous data and of error coding data) in the set from the other DST execution units (recall for this example, the pillar width is 5 and the decode threshold is 3). The DST execution unit decodes the retrieved data slices using the DS error encoding parameters to recapture the corresponding data segment. The DST execution unit then re-encodes the data segment using the DS error encoding parameters to rebuild the corrupted encoded data slice. Once the encoded data slice is rebuilt, the DST execution unit functions as previously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbound distributed storage and/or task (DST) processing section 82 of a DST client module coupled to DST execution units of a distributed storage and task network (DSTN) module via a network 24. The inbound DST processing section 82 includes a de-grouping module 180, a DS (dispersed storage) error decoding module 182, a data de-partitioning module 184, a control module 186, and a distributed task control module 188. Note that the control module 186 and/or the distributed task control module 188 may be separate modules from corresponding ones of outbound DST processing section or may be the same modules.

In an example of operation, the DST execution units have completed execution of corresponding partial tasks on the corresponding slice groupings to produce partial results 102. The inbound DST processing section 82 receives the partial results 102 via the distributed task control module 188. The inbound DST processing section 82 then processes the partial results 102 to produce a final result, or results 104. For example, if the task was to find a specific word or phrase within data, the partial results 102 indicate where in each of the prescribed portions of the data the corresponding DST execution units found the specific word or phrase. The distributed task control module 188 combines the individual partial results 102 for the corresponding portions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82 is retrieving stored data from the DST execution units (i.e., the DSTN module). In this example, the DST execution units output encoded data slices 100 corresponding to the data retrieval requests. The de-grouping module 180 receives retrieved slices 100 and de-groups them to produce encoded data slices per data partition 122. The DS error decoding module 182 decodes, in accordance with DS error encoding parameters, the encoded data slices per data partition 122 to produce data partitions 120.

The data de-partitioning module 184 combines the data partitions 120 into the data 92. The control module 186 controls the conversion of retrieved slices 100 into the data 92 using control signals 190 to each of the modules. For instance, the control module 186 provides de-grouping information to the de-grouping module 180, provides the DS error encoding parameters to the DS error decoding module 182, and provides de-partitioning information to the data de-partitioning module 184.

FIG. 14 is a logic diagram of an example of a method that is executable by distributed storage and task (DST) client module regarding inbound DST processing. The method begins at step 194 where the DST client module receives partial results. The method continues at step 196 where the DST client module retrieves the task corresponding to the partial results. For example, the partial results include header information that identifies the requesting entity, which correlates to the requested task.

The method continues at step 198 where the DST client module determines result processing information based on the task. For example, if the task were to identify a particular word or phrase within the data, the result processing information would indicate to aggregate the partial results for the corresponding portions of the data to produce the final result. As another example, if the task were to count the occurrences of a particular word or phrase within the data, results of processing the information would indicate to add the partial results to produce the final results. The method continues at step 200 where the DST client module processes the partial results in accordance with the result processing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processing of an inbound distributed storage and task (DST) processing section of a DST client module. In general, this is an inverse process of the grouping module of the outbound DST processing section of FIG. 9. Accordingly, for each data partition (e.g., partition #1), the de-grouping module retrieves the corresponding slice grouping from the DST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, which includes the first encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks 1-15); DST execution unit #2 provides a second slice grouping, which includes the second encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks 16-30); DST execution unit #3 provides a third slice grouping, which includes the third encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks 31-45); DST execution unit #3 provides a fourth slice grouping, which includes the fourth encoded slices of each of the sets of encoded slices (e.g., first encoded data slices of error coding (EC) data); and DST execution unit #5 provides a fifth slice grouping, which includes the fifth encoded slices of each of the sets of encoded slices (e.g., first encoded data slices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., received slices 100) using a de-grouping selector 180 controlled by a control signal 190 as shown in the example to produce a plurality of sets of encoded data slices (e.g., retrieved slices for a partition into sets of slices 122). Each set corresponding to a data segment of the data partition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersed storage (DS) error decoding module 182 of an inbound distributed storage and task (DST) processing section. The DS error decoding module 182 includes an inverse per slice security processing module 202, a de-slicing module 204, an error decoding module 206, an inverse segment security module 208, a de-segmenting processing module 210, and a control module 186.

In an example of operation, the inverse per slice security processing module 202, when enabled by the control module 186, unsecures each encoded data slice 122 based on slice de-security information received as control information 190 (e.g., the compliment of the slice security information discussed with reference to FIG. 6) received from the control module 186. The slice security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC verification, etc.), and/or any other type of digital security. For example, when the inverse per slice security processing module 202 is enabled, it verifies integrity information (e.g., a CRC value) of each encoded data slice 122, it decrypts each verified encoded data slice, and decompresses each decrypted encoded data slice to produce slice encoded data 158. When the inverse per slice security processing module 202 is not enabled, it passes the encoded data slices 122 as the sliced encoded data 158 or is bypassed such that the retrieved encoded data slices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 into encoded data segments 156 in accordance with a pillar width of the error correction encoding parameters received as control information 190 from the control module 186. For example, if the pillar width is five, the de-slicing module 204 de-slices a set of five encoded data slices into an encoded data segment 156. The error decoding module 206 decodes the encoded data segments 156 in accordance with error correction decoding parameters received as control information 190 from the control module 186 to produce secure data segments 154. The error correction decoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction decoding parameters identify a specific error correction encoding scheme, specify a pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by the control module 186, unsecures the secured data segments 154 based on segment security information received as control information 190 from the control module 186. The segment security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC, etc.) verification, and/or any other type of digital security. For example, when the inverse segment security processing module 208 is enabled, it verifies integrity information (e.g., a CRC value) of each secure data segment 154, it decrypts each verified secured data segment, and decompresses each decrypted secure data segment to produce a data segment 152. When the inverse segment security processing module 208 is not enabled, it passes the decoded data segment 154 as the data segment 152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 and receives de-segmenting information as control information 190 from the control module 186. The de-segmenting information indicates how the de-segment processing module 210 is to de-segment the data segments 152 into a data partition 120. For example, the de-segmenting information indicates how the rows and columns of data segments are to be rearranged to yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decoding processing of a dispersed error decoding module. A de-slicing module 204 receives at least a decode threshold number of encoded data slices 158 for each data segment in accordance with control information 190 and provides encoded data 156. In this example, a decode threshold is three. As such, each set of encoded data slices 158 is shown to have three encoded data slices per data segment. The de-slicing module 204 may receive three encoded data slices per data segment because an associated distributed storage and task (DST) client module requested retrieving only three encoded data slices per segment or selected three of the retrieved encoded data slices per data segment. As shown, which is based on the unity matrix encoding previously discussed with reference to FIG. 8, an encoded data slice may be a data-based encoded data slice (e.g., DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each data segment in accordance with the error correction decoding parameters of control information 190 to produce secured segments 154. In this example, data segment 1 includes 3 rows with each row being treated as one word for encoding. As such, data segment 1 includes three words: word 1 including data blocks d1 and d2, word 2 including data blocks d16 and d17, and word 3 including data blocks d31 and d32. Each of data segments 2-7 includes three words where each word includes two data blocks. Data segment 8 includes three words where each word includes a single data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of a de-segment processing of an inbound distributed storage and task (DST) processing. In this example, a de-segment processing module 210 receives data segments 152 (e.g., 1-8) and rearranges the data blocks of the data segments into rows and columns in accordance with de-segmenting information of control information 190 to produce a data partition 120. Note that the number of rows is based on the decode threshold (e.g., 3 in this specific example) and the number of columns is based on the number and size of the data blocks.

The de-segmenting module 210 converts the rows and columns of data blocks into the data partition 120. Note that each data block may be of the same size as other data blocks or of a different size. In addition, the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data 92 within an inbound distributed storage and task (DST) processing section. As shown, the data 92 is reconstructed from a plurality of data partitions (1-x, where x is an integer greater than 4). Each data partition (or chunk set of data) is decoded and re-grouped using a de-grouping and decoding function 212 and a de-partition function 214 from slice groupings as previously discussed. For a given data partition, the slice groupings (e.g., at least a decode threshold per data segment of encoded data slices) are received from DST execution units. From data partition to data partition, the ordering of the slice groupings received from the DST execution units may vary as discussed with reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/or retrieval within the distributed computing system. The distributed computing system includes a plurality of distributed storage and/or task (DST) processing client modules 34 (one shown) coupled to a distributed storage and/or task processing network (DSTN) module, or multiple DSTN modules, via a network 24. The DST client module 34 includes an outbound DST processing section 80 and an inbound DST processing section 82. The DSTN module includes a plurality of DST execution units. Each DST execution unit includes a controller 86, memory 88, one or more distributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 that it desires to store in the DSTN module. The data 92 may be a file (e.g., video, audio, text, graphics, etc.), a data object, a data block, an update to a file, an update to a data block, etc. In this instance, the outbound DST processing module 80 converts the data 92 into encoded data slices 216 as will be further described with reference to FIGS. 21-23. The outbound DST processing module 80 sends, via the network 24, to the DST execution units for storage as further described with reference to FIG. 24.

In an example of data retrieval, the DST client module 34 issues a retrieve request to the DST execution units for the desired data 92. The retrieve request may address each DST executions units storing encoded data slices of the desired data, address a decode threshold number of DST execution units, address a read threshold number of DST execution units, or address some other number of DST execution units. In response to the request, each addressed DST execution unit retrieves its encoded data slices 100 of the desired data and sends them to the inbound DST processing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82 receives at least a decode threshold number of encoded data slices 100, it converts the encoded data slices 100 into a data segment. The inbound DST processing section 82 aggregates the data segments to produce the retrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing section 80 of a DST client module coupled to a distributed storage and task network (DSTN) module (e.g., a plurality of DST execution units) via a network 24. The outbound DST processing section 80 includes a data partitioning module 110, a dispersed storage (DS) error encoding module 112, a grouping selector module 114, a control module 116, and a distributed task control module 118.

In an example of operation, the data partitioning module 110 is by-passed such that data 92 is provided directly to the DS error encoding module 112. The control module 116 coordinates the by-passing of the data partitioning module 110 by outputting a bypass 220 message to the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serial manner, a parallel manner, and/or a combination thereof. The DS error encoding module 112 DS error encodes the data in accordance with control information 160 from the control module 116 to produce encoded data slices 218. The DS error encoding includes segmenting the data 92 into data segments, segment security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC, etc.)), error encoding, slicing, and/or per slice security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC, etc.)). The control information 160 indicates which steps of the DS error encoding are active for the data 92 and, for active steps, indicates the parameters for the step. For example, the control information 160 indicates that the error encoding is active and includes error encoding parameters (e.g., pillar width, decode threshold, write threshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 218 of the data segments into pillars of slices 216. The number of pillars corresponds to the pillar width of the DS error encoding parameters. In this example, the distributed task control module 118 facilitates the storage request.

FIG. 22 is a schematic block diagram of an example of a dispersed storage (DS) error encoding module 112 for the example of FIG. 21. The DS error encoding module 112 includes a segment processing module 142, a segment security processing module 144, an error encoding module 146, a slicing module 148, and a per slice security processing module 150. Each of these modules is coupled to a control module 116 to receive control information 160 therefrom.

In an example of operation, the segment processing module 142 receives data 92 and receives segmenting information as control information 160 from the control module 116. The segmenting information indicates how the segment processing module is to segment the data. For example, the segmenting information indicates the size of each data segment. The segment processing module 142 segments the data 92 into data segments 152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the control module 116, secures the data segments 152 based on segment security information received as control information 160 from the control module 116. The segment security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the segment security processing module 144 is enabled, it compresses a data segment 152, encrypts the compressed data segment, and generates a CRC value for the encrypted data segment to produce a secure data segment. When the segment security processing module 144 is not enabled, it passes the data segments 152 to the error encoding module 146 or is bypassed such that the data segments 152 are provided to the error encoding module 146.

The error encoding module 146 encodes the secure data segments in accordance with error correction encoding parameters received as control information 160 from the control module 116. The error correction encoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction encoding parameters identify a specific error correction encoding scheme, specifies a pillar width of five, and specifies a decode threshold of three. From these parameters, the error encoding module 146 encodes a data segment to produce an encoded data segment.

The slicing module 148 slices the encoded data segment in accordance with a pillar width of the error correction encoding parameters. For example, if the pillar width is five, the slicing module slices an encoded data segment into a set of five encoded data slices. As such, for a plurality of data segments, the slicing module 148 outputs a plurality of sets of encoded data slices as shown within encoding and slicing function 222 as described.

The per slice security processing module 150, when enabled by the control module 116, secures each encoded data slice based on slice security information received as control information 160 from the control module 116. The slice security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the per slice security processing module 150 is enabled, it may compress an encoded data slice, encrypt the compressed encoded data slice, and generate a CRC value for the encrypted encoded data slice to produce a secure encoded data slice tweaking. When the per slice security processing module 150 is not enabled, it passes the encoded data slices or is bypassed such that the encoded data slices 218 are the output of the DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillar slice groups utilizing encoding, slicing and pillar grouping function 224 for storage in memory of a distributed storage and task network (DSTN) module. As previously discussed the data 92 is encoded and sliced into a plurality of sets of encoded data slices; one set per data segment. The grouping selector module organizes the sets of encoded data slices into pillars of data slices. In this example, the DS error encoding parameters include a pillar width of 5 and a decode threshold of 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selector module takes the first encoded data slice of each of the sets and forms a first pillar, which may be sent to the first DST execution unit. Similarly, the grouping selector module creates the second pillar from the second slices of the sets; the third pillar from the third slices of the sets; the fourth pillar from the fourth slices of the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributed storage and/or task (DST) execution unit that includes an interface 169, a controller 86, memory 88, one or more distributed task (DT) execution modules 90, and a DST client module 34. A computing core 26 may be utilized to implement the one or more DT execution modules 90 and the DST client module 34. The memory 88 is of sufficient size to store a significant number of encoded data slices (e.g., thousands of slices to hundreds-of-millions of slices) and may include one or more hard drives and/or one or more solid-state memory devices (e.g., flash memory, DRAM, etc.).

In an example of storing a pillar of slices 216, the DST execution unit receives, via interface 169, a pillar of slices 216 (e.g., pillar #1 slices). The memory 88 stores the encoded data slices 216 of the pillar of slices in accordance with memory control information 174 it receives from the controller 86. The controller 86 (e.g., a processing module, a CPU, etc.) generates the memory control information 174 based on distributed storage information (e.g., user information (e.g., user ID, distributed storage permissions, data access permission, etc.), vault information (e.g., virtual memory assigned to user, user group, etc.), etc.). Similarly, when retrieving slices, the DST execution unit receives, via interface 169, a slice retrieval request. The memory 88 retrieves the slice in accordance with memory control information 174 it receives from the controller 86. The memory 88 outputs the slice 100, via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of an inbound distributed storage and/or task (DST) processing section 82 for retrieving dispersed error encoded data 92. The inbound DST processing section 82 includes a de-grouping module 180, a dispersed storage (DS) error decoding module 182, a data de-partitioning module 184, a control module 186, and a distributed task control module 188. Note that the control module 186 and/or the distributed task control module 188 may be separate modules from corresponding ones of an outbound DST processing section or may be the same modules.

In an example of operation, the inbound DST processing section 82 is retrieving stored data 92 from the DST execution units (i.e., the DSTN module). In this example, the DST execution units output encoded data slices corresponding to data retrieval requests from the distributed task control module 188. The de-grouping module 180 receives pillars of slices 100 and de-groups them in accordance with control information 190 from the control module 186 to produce sets of encoded data slices 218. The DS error decoding module 182 decodes, in accordance with the DS error encoding parameters received as control information 190 from the control module 186, each set of encoded data slices 218 to produce data segments, which are aggregated into retrieved data 92. The data de-partitioning module 184 is by-passed in this operational mode via a bypass signal 226 of control information 190 from the control module 186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersed storage (DS) error decoding module 182 of an inbound distributed storage and task (DST) processing section. The DS error decoding module 182 includes an inverse per slice security processing module 202, a de-slicing module 204, an error decoding module 206, an inverse segment security module 208, and a de-segmenting processing module 210. The dispersed error decoding module 182 is operable to de-slice and decode encoded slices per data segment 218 utilizing a de-slicing and decoding function 228 to produce a plurality of data segments that are de-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processing module 202, when enabled by the control module 186 via control information 190, unsecures each encoded data slice 218 based on slice de-security information (e.g., the compliment of the slice security information discussed with reference to FIG. 6) received as control information 190 from the control module 186. The slice de-security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC verification, etc.), and/or any other type of digital security. For example, when the inverse per slice security processing module 202 is enabled, it verifies integrity information (e.g., a CRC value) of each encoded data slice 218, it decrypts each verified encoded data slice, and decompresses each decrypted encoded data slice to produce slice encoded data. When the inverse per slice security processing module 202 is not enabled, it passes the encoded data slices 218 as the sliced encoded data or is bypassed such that the retrieved encoded data slices 218 are provided as the sliced encoded data.

The de-slicing module 204 de-slices the sliced encoded data into encoded data segments in accordance with a pillar width of the error correction encoding parameters received as control information 190 from a control module 186. For example, if the pillar width is five, the de-slicing module de-slices a set of five encoded data slices into an encoded data segment. Alternatively, the encoded data segment may include just three encoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments in accordance with error correction decoding parameters received as control information 190 from the control module 186 to produce secure data segments. The error correction decoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction decoding parameters identify a specific error correction encoding scheme, specify a pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by the control module 186, unsecures the secured data segments based on segment security information received as control information 190 from the control module 186. The segment security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC, etc.) verification, and/or any other type of digital security. For example, when the inverse segment security processing module is enabled, it verifies integrity information (e.g., a CRC value) of each secure data segment, it decrypts each verified secured data segment, and decompresses each decrypted secure data segment to produce a data segment 152. When the inverse segment security processing module 208 is not enabled, it passes the decoded data segment 152 as the data segment or is bypassed. The de-segmenting processing module 210 aggregates the data segments 152 into the data 92 in accordance with control information 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module that includes a plurality of distributed storage and task (DST) execution units (#1 through #n, where, for example, n is an integer greater than or equal to three). Each of the DST execution units includes a DST client module 34, a controller 86, one or more DT (distributed task) execution modules 90, and memory 88.

In this example, the DSTN module stores, in the memory of the DST execution units, a plurality of DS (dispersed storage) encoded data (e.g., 1 through n, where n is an integer greater than or equal to two) and stores a plurality of DS encoded task codes (e.g., 1 through k, where k is an integer greater than or equal to two). The DS encoded data may be encoded in accordance with one or more examples described with reference to FIGS. 3-19 (e.g., organized in slice groupings) or encoded in accordance with one or more examples described with reference to FIGS. 20-26 (e.g., organized in pillar groups). The data that is encoded into the DS encoded data may be of any size and/or of any content. For example, the data may be one or more digital books, a copy of a company's emails, a large-scale Internet search, a video security file, one or more entertainment video files (e.g., television programs, movies, etc.), data files, and/or any other large amount of data (e.g., greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simple function (e.g., a mathematical function, a logic function, an identify function, a find function, a search engine function, a replace function, etc.), a complex function (e.g., compression, human and/or computer language translation, text-to-voice conversion, voice-to-text conversion, etc.), multiple simple and/or complex functions, one or more algorithms, one or more applications, etc. The tasks may be encoded into the DS encoded task code in accordance with one or more examples described with reference to FIGS. 3-19 (e.g., organized in slice groupings) or encoded in accordance with one or more examples described with reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of a DST processing unit issues a DST request to the DSTN module. The DST request may include a request to retrieve stored data, or a portion thereof, may include a request to store data that is included with the DST request, may include a request to perform one or more tasks on stored data, may include a request to perform one or more tasks on data included with the DST request, etc. In the cases where the DST request includes a request to store data or to retrieve data, the client module and/or the DSTN module processes the request as previously discussed with reference to one or more of FIGS. 3-19 (e.g., slice groupings) and/or 20-26 (e.g., pillar groupings). In the case where the DST request includes a request to perform one or more tasks on data included with the DST request, the DST client module and/or the DSTN module process the DST request as previously discussed with reference to one or more of FIGS. 3-19.

In the case where the DST request includes a request to perform one or more tasks on stored data, the DST client module and/or the DSTN module processes the DST request as will be described with reference to one or more of FIGS. 28-39. In general, the DST client module identifies data and one or more tasks for the DSTN module to execute upon the identified data. The DST request may be for a one-time execution of the task or for an on-going execution of the task. As an example of the latter, as a company generates daily emails, the DST request may be to daily search new emails for inappropriate content and, if found, record the content, the email sender(s), the email recipient(s), email routing information, notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributed computing system performing tasks on stored data. In this example, two distributed storage and task (DST) client modules 1-2 are shown: the first may be associated with a user device and the second may be associated with a DST processing unit or a high priority user device (e.g., high priority clearance user, system administrator, etc.). Each DST client module includes a list of stored data 234 and a list of tasks codes 236. The list of stored data 234 includes one or more entries of data identifying information, where each entry identifies data stored in the DSTN module 22. The data identifying information (e.g., data ID) includes one or more of a data file name, a data file directory listing, DSTN addressing information of the data, a data object identifier, etc. The list of tasks 236 includes one or more entries of task code identifying information, when each entry identifies task codes stored in the DSTN module 22. The task code identifying information (e.g., task ID) includes one or more of a task file name, a task file directory listing, DSTN addressing information of the task, another type of identifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are each smaller in number of entries for the first DST client module than the corresponding lists of the second DST client module. This may occur because the user device associated with the first DST client module has fewer privileges in the distributed computing system than the device associated with the second DST client module. Alternatively, this may occur because the user device associated with the first DST client module serves fewer users than the device associated with the second DST client module and is restricted by the distributed computing system accordingly. As yet another alternative, this may occur through no restraints by the distributed computing system, it just occurred because the operator of the user device associated with the first DST client module has selected fewer data and/or fewer tasks than the operator of the device associated with the second DST client module.

In an example of operation, the first DST client module selects one or more data entries 238 and one or more tasks 240 from its respective lists (e.g., selected data ID and selected task ID). The first DST client module sends its selections to a task distribution module 232. The task distribution module 232 may be within a stand-alone device of the distributed computing system, may be within the user device that contains the first DST client module, or may be within the DSTN module 22.

Regardless of the task distribution module's location, it generates DST allocation information 242 from the selected task ID 240 and the selected data ID 238. The DST allocation information 242 includes data partitioning information, task execution information, and/or intermediate result information. The task distribution module 232 sends the DST allocation information 242 to the DSTN module 22. Note that one or more examples of the DST allocation information will be discussed with reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 to identify the stored DS encoded data (e.g., DS error encoded data 2) and to identify the stored DS error encoded task code (e.g., DS error encoded task code 1). In addition, the DSTN module 22 interprets the DST allocation information 242 to determine how the data is to be partitioned and how the task is to be partitioned. The DSTN module 22 also determines whether the selected DS error encoded data 238 needs to be converted from pillar grouping to slice grouping. If so, the DSTN module 22 converts the selected DS error encoded data into slice groupings and stores the slice grouping DS error encoded data by overwriting the pillar grouping DS error encoded data or by storing it in a different location in the memory of the DSTN module 22 (i.e., does not overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in the DST allocation information 242 and sends the portions to selected DST execution units of the DSTN module 22. Each of the selected DST execution units performs its partial task(s) on its slice groupings to produce partial results. The DSTN module 22 collects the partial results from the selected DST execution units and provides them, as result information 244, to the task distribution module. The result information 244 may be the collected partial results, one or more final results as produced by the DSTN module 22 from processing the partial results in accordance with the DST allocation information 242, or one or more intermediate results as produced by the DSTN module 22 from processing the partial results in accordance with the DST allocation information 242.

The task distribution module 232 receives the result information 244 and provides one or more final results 104 therefrom to the first DST client module. The final result(s) 104 may be result information 244 or a result(s) of the task distribution module's processing of the result information 244.

In concurrence with processing the selected task of the first DST client module, the distributed computing system may process the selected task(s) of the second DST client module on the selected data(s) of the second DST client module. Alternatively, the distributed computing system may process the second DST client module's request subsequent to, or preceding, that of the first DST client module. Regardless of the ordering and/or parallel processing of the DST client module requests, the second DST client module provides its selected data 238 and selected task 240 to a task distribution module 232. If the task distribution module 232 is a separate device of the distributed computing system or within the DSTN module, the task distribution modules 232 coupled to the first and second DST client modules may be the same module. The task distribution module 232 processes the request of the second DST client module in a similar manner as it processed the request of the first DST client module.

FIG. 29 is a schematic block diagram of an embodiment of a task distribution module 232 facilitating the example of FIG. 28. The task distribution module 232 includes a plurality of tables it uses to generate distributed storage and task (DST) allocation information 242 for selected data and selected tasks received from a DST client module. The tables include data storage information 248, task storage information 250, distributed task (DT) execution module information 252, and task

sub-task mapping information 246.

The data storage information table 248 includes a data identification (ID) field 260, a data size field 262, an addressing information field 264, distributed storage (DS) information 266, and may further include other information regarding the data, how it is stored, and/or how it can be processed. For example, DS encoded data #1 has a data ID of 1, a data size of AA (e.g., a byte size of a few Terabytes or more), addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1; and SLC_1. In this example, the addressing information may be a virtual address corresponding to the virtual address of the first storage word (e.g., one or more bytes) of the data and information on how to calculate the other addresses, may be a range of virtual addresses for the storage words of the data, physical addresses of the first storage word or the storage words of the data, may be a list of slice names of the encoded data slices of the data, etc. The DS parameters may include identity of an error encoding scheme, decode threshold/pillar width (e.g., 3/5 for the first data entry), segment security information (e.g., SEG_1), per slice security information (e.g., SLC_1), and/or any other information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification (ID) field 268, a task size field 270, an addressing information field 272, distributed storage (DS) information 274, and may further include other information regarding the task, how it is stored, and/or how it can be used to process data. For example, DS encoded task #2 has a task ID of 2, a task size of XY, addressing information of Addr_2_XY, and DS parameters of 3/5; SEG_2; and SLC_2. In this example, the addressing information may be a virtual address corresponding to the virtual address of the first storage word (e.g., one or more bytes) of the task and information on how to calculate the other addresses, may be a range of virtual addresses for the storage words of the task, physical addresses of the first storage word or the storage words of the task, may be a list of slices names of the encoded slices of the task code, etc. The DS parameters may include identity of an error encoding scheme, decode threshold/pillar width (e.g., 3/5 for the first data entry), segment security information (e.g., SEG_2), per slice security information (e.g., SLC_2), and/or any other information regarding how the task was encoded into encoded task slices. Note that the segment and/or the per-slice security information include a type of encryption (if enabled), a type of compression (if enabled), watermarking information (if enabled), and/or an integrity check scheme (if enabled).

The task

sub-task mapping information table 246 includes a task field 256 and a sub-task field 258. The task field 256 identifies a task stored in the memory of a distributed storage and task network (DSTN) module and the corresponding sub-task fields 258 indicates whether the task includes sub-tasks and, if so, how many and if any of the sub-tasks are ordered. In this example, the task

sub-task mapping information table 246 includes an entry for each task stored in memory of the DSTN module (e.g., task 1 through task k). In particular, this example indicates that task 1 includes 7 sub-tasks; task 2 does not include sub-tasks, and task k includes r number of sub-tasks (where r is an integer greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field 276, a DT execution module ID field 278, and a DT execution module capabilities field 280. The DST execution unit ID field 276 includes the identity of DST units in the DSTN module. The DT execution module ID field 278 includes the identity of each DT execution unit in each DST unit. For example, DST unit 1 includes three DT executions modules (e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280 includes identity of the capabilities of the corresponding DT execution unit. For example, DT execution module 1_1 includes capabilities X, where X includes one or more of MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, memory information (e.g., type, size, availability, etc.), and/or any information germane to executing one or more tasks.

From these tables, the task distribution module 232 generates the DST allocation information 242 to indicate where the data is stored, how to partition the data, where the task is stored, how to partition the task, which DT execution units should perform which partial task on which data partitions, where and how intermediate results are to be stored, etc. If multiple tasks are being performed on the same data or different data, the task distribution module factors such information into its generation of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computing system performing tasks on stored data as a task flow 318. In this example, selected data 92 is data 2 and selected tasks are tasks 1, 2, and 3. Task 1 corresponds to analyzing translation of data from one language to another (e.g., human language or computer language); task 2 corresponds to finding specific words and/or phrases in the data; and task 3 corresponds to finding specific translated words and/or phrases in translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identify non-words (non-ordered); task 1_2 —identify unique words (non-ordered); task 1_3—translate (non-ordered); task 1_4 —translate back (ordered after task 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task 1_6—determine non-word translation errors (ordered after task 1_5 and 1_1); and task 1_7 —determine correct translations (ordered after 1_5 and 1_2). The sub-task further indicates whether they are an ordered task (i.e., are dependent on the outcome of another task) or non-order (i.e., are independent of the outcome of another task). Task 2 does not include sub-tasks and task 3 includes two sub-tasks: task 3_1 translate; and task 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze data for translation accuracies, translation errors, translation anomalies, occurrence of specific words or phrases in the data, and occurrence of specific words or phrases on the translated data. Graphically, the data 92 is translated 306 into translated data 282; is analyzed for specific words and/or phrases 300 to produce a list of specific words and/or phrases 286; is analyzed for non-words 302 (e.g., not in a reference dictionary) to produce a list of non-words 290; and is analyzed for unique words 316 included in the data 92 (i.e., how many different words are included in the data) to produce a list of unique words 298. Each of these tasks is independent of each other and can therefore be processed in parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specific translated words and/or phrases 304 to produce a list of specific translated words and/or phrases 288. The translated data 282 is translated back 308 (e.g., sub-task 1_4) into the language of the original data to produce re-translated data 284. These two tasks are dependent on the translate task (e.g., task 1_3) and thus must be ordered after the translation task, which may be in a pipelined ordering or a serial ordering. The re-translated data 284 is then compared 310 with the original data 92 to find words and/or phrases that did not translate (one way and/or the other) properly to produce a list of incorrectly translated words 294. As such, the comparing task (e.g., sub-task 1_5) 310 is ordered after the translation 306 and re-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the list of non-words 290 to identify words that were not properly translated because the words are non-words to produce a list of errors due to non-words 292. In addition, the list of words incorrectly translated 294 is compared 314 to the list of unique words 298 to identify unique words that were properly translated to produce a list of correctly translated words 296. The comparison may also identify unique words that were not properly translated to produce a list of unique words that were not properly translated. Note that each list of words (e.g., specific words and/or phrases, non-words, unique words, translated words and/or phrases, etc.,) may include the word and/or phrase, how many times it is used, where in the data it is used, and/or any other information requested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing data and task codes for the example of FIG. 30. As shown, DS encoded data 2 is stored as encoded data slices across the memory (e.g., stored in memories 88) of DST execution units 1-5; the DS encoded task code 1 (of task 1) and DS encoded task 3 are stored as encoded task slices across the memory of DST execution units 1-5; and DS encoded task code 2 (of task 2) is stored as encoded task slices across the memory of DST execution units 3-7. As indicated in the data storage information table and the task storage information table of FIG. 29, the respective data/task has DS parameters of 3/5 for their decode threshold/pillar width; hence spanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST) allocation information 242 for the example of FIG. 30. The DST allocation information 242 includes data partitioning information 320, task execution information 322, and intermediate result information 324. The data partitioning information 320 includes the data identifier (ID), the number of partitions to split the data into, address information for each data partition, and whether the DS encoded data has to be transformed from pillar grouping to slice grouping. The task execution information 322 includes tabular information having a task identification field 326, a task ordering field 328, a data partition field ID 330, and a set of DT execution modules 332 to use for the distributed task processing per data partition. The intermediate result information 324 includes tabular information having a name ID field 334, an ID of the DST execution unit assigned to process the corresponding intermediate result 336, a scratch pad storage field 338, and an intermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to be distributedly performed on data 2, the data partitioning information includes the ID of data 2. In addition, the task distribution module determines whether the DS encoded data 2 is in the proper format for distributed computing (e.g., was stored as slice groupings). If not, the task distribution module indicates that the DS encoded data 2 format needs to be changed from the pillar grouping format to the slice grouping format, which will be done by the DSTN module. In addition, the task distribution module determines the number of partitions to divide the data into (e.g., 2_1 through 2_z) and addressing information for each partition.

The task distribution module generates an entry in the task execution information section for each sub-task to be performed. For example, task 1_1 (e.g., identify non-words on the data) has no task ordering (i.e., is independent of the results of other sub-tasks), is to be performed on data partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 search for non-words in data partitions 2_1 through 2_z to produce task 1_1 intermediate results (R1-1, which is a list of non-words). Task 1_2 (e.g., identify unique words) has similar task execution information as task 1_1 to produce task 1_2 intermediate results (R1-2, which is the list of unique words).

Task 1_3 (e.g., translate) includes task execution information as being non-ordered (i.e., is independent), having DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 and having DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate data partitions 2_5 through 2_z to produce task 1_3 intermediate results (R1-3, which is the translated data). In this example, the data partitions are grouped, where different sets of DT execution modules perform a distributed sub-task (or task) on each data partition group, which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to be executed on task 1_3 's intermediate result (e.g., R1-3_1) (e.g., the translated data). DT execution modules 1_1, 2_1, 3_1, 4 _(—1), and 5_1 are allocated to translate back task 1_3 intermediate result partitions R1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back task 1_3 intermediate result partitions R1-3_5 through R1-3_z to produce task 1-4 intermediate results (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translation errors) is ordered after task 1_4 and is to be executed on task 1_4's intermediate results (R4-1) and on the data. DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1 through 2_z) with partitions of task 1-4 intermediate results partitions R1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5, which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered after tasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5's intermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1 intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5 intermediate results partitions (R1-5_1 through R1-5_z) to produce task 1_6 intermediate results (R1-6, which is the list translation errors due to non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered after tasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5's intermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2 intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5 intermediate results partitions (R1-5_1 through R1-5_z) to produce task 1_7 intermediate results (R1-7, which is the list of correctly translated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering (i.e., is independent of the results of other sub-tasks), is to be performed on data partitions 2_1 through 2_z by DT execution modules 3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1, 4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in data partitions 2_1 through 2_z to produce task 2 intermediate results (R2, which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) is ordered after task 1_3 (e.g., translate) is to be performed on partitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 search for specific translated words and/or phrases in the partitions of the translated data (R1-3_1 through R1-3_z) to produce task 3_2 intermediate results (R3-2, which is a list of specific translated words and/or phrases).

For each task, the intermediate result information indicates which DST unit is responsible for overseeing execution of the task and, if needed, processing the partial results generated by the set of allocated DT execution units. In addition, the intermediate result information indicates a scratch pad memory for the task and where the corresponding intermediate results are to be stored. For example, for intermediate result R1-1 (the intermediate result of task 1_1), DST unit 1 is responsible for overseeing execution of the task 1_1 and coordinates storage of the intermediate result as encoded intermediate result slices stored in memory of DST execution units 1-5. In general, the scratch pad is for storing non-DS encoded intermediate results and the intermediate result storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage and task network (DSTN) module performing the example of FIG. 30. In FIG. 33, the DSTN module accesses the data 92 and partitions it into a plurality of partitions 1-z in accordance with distributed storage and task network (DST) allocation information. For each data partition, the DSTN identifies a set of its DT (distributed task) execution modules 90 to perform the task (e.g., identify non-words (i.e., not in a reference dictionary) within the data partition) in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules 90 may be the same, different, or a combination thereof (e.g., some data partitions use the same set while other data partitions use different sets).

For the first data partition, the first set of DT execution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information of FIG. 32) executes task 1_1 to produce a first partial result 102 of non-words found in the first data partition. The second set of DT execution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information of FIG. 32) executes task 1_1 to produce a second partial result 102 of non-words found in the second data partition. The sets of DT execution modules (as per the DST allocation information) perform task 1_1 on the data partitions until the “z” set of DT execution modules performs task 1_1 on the “zth” data partition to produce a “zth” partial result 102 of non-words found in the “zth” data partition.

As indicated in the DST allocation information of FIG. 32, DST execution unit 1 is assigned to process the first through “zth” partial results to produce the first intermediate result (R1-1), which is a list of non-words found in the data. For instance, each set of DT execution modules 90 stores its respective partial result in the scratchpad memory of DST execution unit 1 (which is identified in the DST allocation or may be determined by DST execution unit 1). A processing module of DST execution 1 is engaged to aggregate the first through “zth” partial results to produce the first intermediate result (e.g., R1_1). The processing module stores the first intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice grouping based DS error encode the first intermediate result (e.g., the list of non-words). To begin the encoding, the DST client module determines whether the list of non-words is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the first intermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1 through R1-1_m). If the first intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the first intermediate result, or for the first intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_2 (e.g., find unique words) on the data 92. To begin, the DSTN module accesses the data 92 and partitions it into a plurality of partitions 1-z in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules to perform task 1_2 in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules may be the same, different, or a combination thereof. For the data partitions, the allocated set of DT execution modules executes task 1_2 to produce a partial results (e.g., 1^(st) through “zth”) of unique words found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST execution unit 1 is assigned to process the first through “zth” partial results 102 of task 1_2 to produce the second intermediate result (R1-2), which is a list of unique words found in the data 92. The processing module of DST execution 1 is engaged to aggregate the first through “zth” partial results of unique words to produce the second intermediate result. The processing module stores the second intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice grouping based DS error encode the second intermediate result (e.g., the list of non-words). To begin the encoding, the DST client module determines whether the list of unique words is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the second intermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1 through R1-2_m). If the second intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the second intermediate result, or for the second intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_3 (e.g., translate) on the data 92. To begin, the DSTN module accesses the data 92 and partitions it into a plurality of partitions 1-z in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules to perform task 1_3 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate data partitions 2_5 through 2_z). For the data partitions, the allocated set of DT execution modules 90 executes task 1_3 to produce partial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST execution unit 2 is assigned to process the first through “zth” partial results of task 1_3 to produce the third intermediate result (R1-3), which is translated data. The processing module of DST execution 2 is engaged to aggregate the first through “zth” partial results of translated data to produce the third intermediate result. The processing module stores the third intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice grouping based DS error encode the third intermediate result (e.g., translated data). To begin the encoding, the DST client module partitions the third intermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1 through R1-3_y). For each partition of the third intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 2-6 per the DST allocation information).

As is further shown in FIG. 35, the DSTN module is performing task 1_4 (e.g., retranslate) on the translated data of the third intermediate result. To begin, the DSTN module accesses the translated data (from the scratchpad memory or from the intermediate result memory and decodes it) and partitions it into a plurality of partitions in accordance with the DST allocation information. For each partition of the third intermediate result, the DSTN identifies a set of its DT execution modules 90 to perform task 1_4 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated to translate back partitions R1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitions R1-3_5 through R1-3_z). For the partitions, the allocated set of DT execution modules executes task 1_4 to produce partial results 102 (e.g., 1st through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST execution unit 3 is assigned to process the first through “zth” partial results of task 1_4 to produce the fourth intermediate result (R1-4), which is retranslated data. The processing module of DST execution 3 is engaged to aggregate the first through “zth” partial results of retranslated data to produce the fourth intermediate result. The processing module stores the fourth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 3.

DST execution unit 3 engages its DST client module to slice grouping based DS error encode the fourth intermediate result (e.g., retranslated data). To begin the encoding, the DST client module partitions the fourth intermediate result (R1-4) into a plurality of partitions (e.g., R1-4_1 through R1-4_z). For each partition of the fourth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 3-7 per the DST allocation information).

In FIG. 36, a distributed storage and task network (DSTN) module is performing task 1_5 (e.g., compare) on data 92 and retranslated data of FIG. 35. To begin, the DSTN module accesses the data 92 and partitions it into a plurality of partitions in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. The DSTN module also accesses the retranslated data from the scratchpad memory, or from the intermediate result memory and decodes it, and partitions it into a plurality of partitions in accordance with the DST allocation information. The number of partitions of the retranslated data corresponds to the number of partitions of the data.

For each pair of partitions (e.g., data partition 1 and retranslated data partition 1), the DSTN identifies a set of its DT execution modules 90 to perform task 1_5 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pair of partitions, the allocated set of DT execution modules executes task 1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of a list of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST execution unit 1 is assigned to process the first through “zth” partial results of task 1_5 to produce the fifth intermediate result (R1-5), which is the list of incorrectly translated words and/or phrases. In particular, the processing module of DST execution 1 is engaged to aggregate the first through “zth” partial results of the list of incorrectly translated words and/or phrases to produce the fifth intermediate result. The processing module stores the fifth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice grouping based DS error encode the fifth intermediate result. To begin the encoding, the DST client module partitions the fifth intermediate result (R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). For each partition of the fifth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task 1_6 (e.g., translation errors due to non-words) on the list of incorrectly translated words and/or phrases (e.g., the fifth intermediate result R1-5) and the list of non-words (e.g., the first intermediate result R1-1). To begin, the DSTN module accesses the lists and partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partition R1-5_1), the DSTN identifies a set of its DT execution modules 90 to perform task 1_6 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pair of partitions, the allocated set of DT execution modules executes task 1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of a list of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32, DST execution unit 2 is assigned to process the first through “zth” partial results of task 1_6 to produce the sixth intermediate result (R1-6), which is the list of incorrectly translated words and/or phrases due to non-words. In particular, the processing module of DST execution 2 is engaged to aggregate the first through “zth” partial results of the list of incorrectly translated words and/or phrases due to non-words to produce the sixth intermediate result. The processing module stores the sixth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice grouping based DS error encode the sixth intermediate result. To begin the encoding, the DST client module partitions the sixth intermediate result (R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6 ₁₃ z). For each partition of the sixth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task 1_7 (e.g., correctly translated words and/or phrases) on the list of incorrectly translated words and/or phrases (e.g., the fifth intermediate result R1-5) and the list of unique words (e.g., the second intermediate result R1-2). To begin, the DSTN module accesses the lists and partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partition R1-5_1), the DSTN identifies a set of its DT execution modules 90 to perform task 1_7 in accordance with the DST allocation information (e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pair of partitions, the allocated set of DT execution modules executes task 1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of a list of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST execution unit 3 is assigned to process the first through “zth” partial results of task 1_7 to produce the seventh intermediate result (R1-7), which is the list of correctly translated words and/or phrases. In particular, the processing module of DST execution 3 is engaged to aggregate the first through “zth” partial results of the list of correctly translated words and/or phrases to produce the seventh intermediate result. The processing module stores the seventh intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 3.

DST execution unit 3 engages its DST client module to slice grouping based DS error encode the seventh intermediate result. To begin the encoding, the DST client module partitions the seventh intermediate result (R1-7) into a plurality of partitions (e.g., R1-7_1 through R1-7_z). For each partition of the seventh intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module is performing task 2 (e.g., find specific words and/or phrases) on the data 92. To begin, the DSTN module accesses the data and partitions it into a plurality of partitions 1-z in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules 90 to perform task 2 in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules may be the same, different, or a combination thereof. For the data partitions, the allocated set of DT execution modules executes task 2 to produce partial results 102 (e.g., 1^(st) through “zth”) of specific words and/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST execution unit 7 is assigned to process the first through “zth” partial results of task 2 to produce task 2 intermediate result (R2), which is a list of specific words and/or phrases found in the data. The processing module of DST execution 7 is engaged to aggregate the first through “zth” partial results of specific words and/or phrases to produce the task 2 intermediate result. The processing module stores the task 2 intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice grouping based DS error encode the task 2 intermediate result. To begin the encoding, the DST client module determines whether the list of specific words and/or phrases is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the task 2 intermediate result (R2) into a plurality of partitions (e.g., R2_1 through R2_m). If the task 2 intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the task 2 intermediate result, or for the task 2 intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module is performing task 3 (e.g., find specific translated words and/or phrases) on the translated data (R1-3). To begin, the DSTN module accesses the translated data (from the scratchpad memory or from the intermediate result memory and decodes it) and partitions it into a plurality of partitions in accordance with the DST allocation information. For each partition, the DSTN identifies a set of its DT execution modules to perform task 3 in accordance with the DST allocation information. From partition to partition, the set of DT execution modules may be the same, different, or a combination thereof. For the partitions, the allocated set of DT execution modules 90 executes task 3 to produce partial results 102 (e.g., 1 ^(st) through “zth”) of specific translated words and/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST execution unit 5 is assigned to process the first through “zth” partial results of task 3 to produce task 3 intermediate result (R3), which is a list of specific translated words and/or phrases found in the translated data. In particular, the processing module of DST execution 5 is engaged to aggregate the first through “zth” partial results of specific translated words and/or phrases to produce the task 3 intermediate result. The processing module stores the task 3 intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 7.

DST execution unit 5 engages its DST client module to slice grouping based DS error encode the task 3 intermediate result. To begin the encoding, the DST client module determines whether the list of specific translated words and/or phrases is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the task 3 intermediate result (R3) into a plurality of partitions (e.g., R3_1 through R3_m). If the task 3 intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3 intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information into final results 104 for the example of FIG. 30. In this example, the result information includes the list of specific words and/or phrases found in the data (task 2 intermediate result), the list of specific translated words and/or phrases found in the data (task 3 intermediate result), the list of non-words found in the data (task 1 first intermediate result R1-1), the list of unique words found in the data (task 1 second intermediate result R1-2), the list of translation errors due to non-words (task 1 sixth intermediate result R1-6), and the list of correctly translated words and/or phrases (task 1 seventh intermediate result R1-7). The task distribution module provides the result information to the requesting DST client module as the results 104.

FIG. 40A is a diagram of an embodiment of a structure of a large data object 350 where the large data object 350 is divided into data regions 1-N. The large data object 350 includes at least one of a multimedia file, a video file, an audio file, a text file, an image file, a drawing file, etc. The large data object 350 may include as many as 100 MB or more. A data object storage tracking table 352 is created with regards to storage of the large data object 350 and a dispersed storage network. The data object storage tracking table 352 includes an available region(s) field 354, a region(s) in or awaiting write process and not available field 356, and a region(s) in delete process and not available field 358.

The fields of the data object storage tracking table 352 are utilized to track status of storage of the data regions of the large data object 350. For example, the available region(s) field 354 includes data region identifiers associated with data regions that are available for retrieval. As another example, the region(s) in or awaiting write process and not available field 356 includes other data region identifiers associated with other data regions that are unavailable for retrieval when associated with an open write transaction. Such an open write transaction is associated with a multi-step writing process (e.g., issuing write slice requests, receiving write slice responses, issuing commit transaction requests) to store the other data regions. As yet another example, the region(s) in delete process and not available field 358 includes still other data region identifiers associated with still other data regions that are unavailable for retrieval when associated with an open delete transaction (e.g., issuing delete slice requests, receiving delete slice responses, issuing commit transaction requests).

A mapping of the data regions may be generated for the very large data object and the mapping may be stored in at least one of a local memory of an associated computing device and as a set of encoded mapping slices in storage units of the dispersed storage network. Subsequent to storage of the large data object in the dispersed storage network, the very large data object may be edited by one of revising one or more of the data regions and deleting one or more of the data regions. The data object storage tracking table 352 may be updated when the very large data object is edited.

FIG. 40B is a diagram of an embodiment of a structure of a data object storage tracking table 352 that includes an available region(s) field 354, a region(s) in or awaiting write process and not available field 356, and a region(s) in delete process and not available field 358. The data object storage tracking table 352 is associated with a very large data object that is stored as a plurality of sets of encoded data slices in a dispersed storage network (DSN). The data object storage tracking table 352 may be utilized to identify storage locations of the very large data object within the DSN. The very large data object may be stored as a plurality of data regions within the DSN where each data region includes a plurality of data segments. Each data segment of the plurality of data segments is encoded using a dispersed storage error coding function to produce a set of encoded data slices of the plurality of sets of encoded data slices. For example, a first grouping of sets of encoded data slices is produced corresponding to a first data region and a second grouping of sets of encoded data slices is produced corresponding to a second data region. Each region of the one or more regions is stored in the DSN at a storage location corresponding to a dispersed storage (DS) address for the region. Each region may be uniquely identified by a region identifier (ID). The data object storage tracking table 352 may be stored in the DSN as a set of table slices at a storage location that includes a table DS address. At least one of a directory and an index may be utilized to identify the table DS address based on an object name (e.g., data ID) for the very large data object.

The available regions field 354 identifies visible regions, if any, by one or more region entries. A data region is visible when each data segment associated with the data region includes at least a write threshold number of favorably committed encoded data slices stored in the DSN. A favorably committed encoded data slice is visible for retrieving from a storage unit of the DSN when the encoded data slice has been written to the DSN and is associated with an executed commit transaction request.

Each region entry includes a region ID field (e.g., region 1), a DS address field (e.g., F530), a region size field (e.g., 100 M), and a segment size field (e.g., 10 M). A region ID entry is included in the region ID field to uniquely identify the region. A DS address entry is included in the DS address field to identify a storage location within the DSN of a first data segment of the plurality of data segments associated with the region. For example, the DS address entry identifies a source name (e.g., F530) for a first data segment. Source names corresponding to other data segments of the one or more data segments of the region may be generated based on the source name for the first data segment (e.g., incrementing a segment field entry by one for each sequential data segment of the plurality of data segments of the region). A region size entry of the region size field indicates a size of the region (e.g., 100 M bytes). A segment size entry of the segment size field indicates a size (e.g., 10 M bytes) of each data segment of the plurality of data segments of the region. A number of data segments may be determined by dividing the region size entry by the segment size entry.

The regions in or awaiting write process and not available field 356 identifies open write transactions with regards to the very large data object. An open write transaction includes a write transaction that is in progress for a data region but has not yet produced visibility of the data region. The regions in or awaiting write process and not available field 356 includes a subsection for each, if any, transaction that is associated with at least one data region of an open write transaction. Each subsection of the open write transaction section includes a transaction identifier (e.g., 3000) and a region entry for each data region associated with the open write transaction (e.g., region 30, DS address 4750, region size 500 M, and segment size 100 M).

The regions in delete process and not available field 358 identifies open delete transactions with regards to the very large data object. An open delete transaction includes a delete transaction that is in progress for a data region but has not yet produced full deletion of the data region. The regions in delete process and not available field 358 includes a subsection for each, if any, transaction that is associated with at least one data region of an open delete transaction. Each subsection includes a transaction identifier (ID) and a region entry for each data region associated with the open delete transaction (e.g., region 15, DS address D990, region size 1 G, and segment size 100 M).

FIGS. 40C, and 40E-40J are schematic block diagrams of an embodiment of a dispersed storage network (DSN) that illustrate steps of an example of storing data in a dispersed storage network (DSN). The DSN includes distributed storage and task (DST) client modules 1 and 2 and the network 24 of FIG. 1, and a set of dispersed storage (DS) units 1-n. Each DS unit may be the DST execution unit 36 of FIG. 1. Each DST client module includes the outbound DST processing 80 and the inbound DST processing 82 of FIG. 3. Each DS unit includes the processing module 84 and the memory 88 of FIG. 3.

In the example of storing data, as illustrated in FIG. 40C, the outbound DST processing 80 of a DST client module 1 receives a write request regarding a very large data object A. The outbound DST processing 80 determines whether the write request is an initial write request for the very large data object or a write request for editing the very large data object. As a specific example, the outbound DST processing 80 retrieves an indication from the write request. As another specific example, the outbound DST processing 80 searches for a data object storage tracking table A (e.g., associated with the very large data object A) and, when it is not found, indicates that the write request is the initial write request. The searching includes at least one of accessing a local memory of the DST client module 1 and accessing a directory using an identifier of the very large data object A to determine whether a DS address exists for the data object storage tracking table A.

When the write request is the initial write request, the outbound DST processing 80 divides the very large data object A into a plurality of data regions. The data regions may be of a common size or of different sizes. The outbound DST processing 80 generates the data object storage tracking table A to include an empty available regions field 354 and an empty regions in or awaiting write process and not available field for open transaction 1, where at least one data region is to be written utilizing transaction 1. Alternatively, or in addition to, the outbound DST processing 80 generates one or more other transactions for storing other data regions. For example, FIG. 40D illustrates an example of writing data regions where a first data region is written with a first transaction, second and third data regions are written with a second transaction, a first sub portion of a fourth data region is written with a third transaction, and a second sub portion of the fourth data region is written with a fourth transaction.

Returning to FIG. 40C, having generated the data object storage tracking table A, the outbound DST processing 80 dispersed storage error encodes the data object storage tracking table A to produce a set of encoded table slices A_1 through A_n. The outbound DST processing 80 issues, via the network 24, write table slice requests 360 to the set of DS units 1-n as write table slice requests 1-n to write the set of encoded table slices to the DS units 1-n. For example, the processing module 84 of DS unit 2 receives write table slice request 2 and stores the encoded table slice A_2 in the memory 88 of DS unit 2.

FIG. 40D is a diagram of an example of writing data regions. In an example of operation, transaction number 2 is generated for writing data regions 2 and 3 to dispersed storage (DS) storage units. Data region 2 is divided into data segments of data region 2 which are disperse storage error encoded to produce first sets of encoded data slices. Data region 3 is divided into data segments of data region 3 which are disperse storage error encoded to produce second sets of encoded data slices. DSN write requests are sent, which include the transaction number 2 , regarding storing the first and second sets of encoded data slices to the DS units. When at least a write threshold number of write responses is received for each of the first and second sets of encoded data slices, a data object storage tracking table A is updated to indicate that the first and second data regions are available. For example, entries of data regions 1 and 2 are included in an available regions field of the data object storage tracking table A.

FIG. 40E is a diagram of an example of writing a data region 1 to a set of storage units 1-n of a dispersed storage network (DSN). The data region 1 is associated with a transaction 1 and is divided into a plurality of data segments 1-x. The plurality of data segments 1-x are each disperse storage error encoded to produce a plurality of sets of encoded data slices 1-1 through 1-n, 2-1 through 2-n, etc. through x-1 through x-n. Dispersed storage network (DSN) write requests regarding storing the plurality of sets of encoded data slices are sent to the storage units 1-n. Prior to storage of all of the encoded data slices, the data region 1 is associated with an open write phase for transaction 1 such that data region 1 is not available for retrieval. When at least a write threshold number of write responses is received from the set of storage units 1-n for each of the plurality of sets of encoded data slices, a data object storage tracking table A is updated to indicate that the first data region is available for retrieval.

FIG. 40F illustrates a continuation of the example of storing data. When the first and second data regions are to be associated with first and second transactions, for the first data region, the outbound DST processing 80 divides the first data region into data segments and disperse storage error encodes the data segments to produce sets of encoded data slices. The outbound DST processing 80 sends write slice requests 362 that includes write slice requests 1-n as DSN write requests regarding storing the sets of encoded data slices to the set of DS units 1-n. For example, DS unit 1 stores data slice A_1_1_1 for data object A, region 1, segment 1, and slice pillar index 1 and stores data slice A_1_2_1 for segment 2 etc.

The outbound DST processing 80 updates the data object storage tracking table A to indicate that data region 1 is not available and that data region 1 is associated with open write transaction 1. As a specific example, the inbound DST processing 82 of DST client module 1 retrieves at least a decode threshold number of encoded table slices of the set of encoded table slices A_1 through A_n and decodes the at least a decode threshold number of encoded table slices to recapture the data object storage tracking table A. The outbound DST processing 80 updates the data object storage tracking table A to produce an updated data object storage tracking table A (e.g., associating data region 1 with open write transaction 1). Having produced the updated data object storage tracking table A, the outbound DST processing 80 disperse storage error encodes the updated data object storage tracking table to produce a set of updated encoded table slices A_1 through A_n and writes the set of updated encoded table slices to the set of DS units 1-n.

FIG. 40G illustrates a continuation of the example of storing data. When at least a write threshold number of write slice responses 364 (e.g., write slice responses 1-n) is received for each of the sets of encoded data slices, the outbound DST processing 80 updates the data object storage tracking table A to indicate that the first data region is available for retrieval (e.g., associating data region 1 with the visible regions 354. The outbound DST processing 80 further updates the data object storing tracking table A to indicate that transaction 2 is an open write transaction, where transaction 2 is associated with data region 2. The outbound DST processing 80 encodes the updated data object storing tracking table A to produce updated encoded table slices A_1 through A_n. The outbound DST processing 80 issues write table slice requests 360 which includes write table slice requests 1-n to the set of DS units 1-n. Each DS unit stores a corresponding encoded table slice in a corresponding memory 88.

FIG. 40H illustrates a continuation of the example of storing data where a very large data object A request is received by the DST client module 2 when storing all of the data regions of the very large data object A has not been completed (e.g., only data region 1 is visible). The inbound DST processing 82 of DST client module 2 performs at least one of a directory lookup and an index lookup to identify the DS address of the data object storage tracking table A based on the identifier of the very large data object A. The inbound DST processing 82 issues read slice requests 366 to the set of DS units 1-n, where the read slice requests 366 includes read slice requests 1-n based on the DS address of the data object storage tracking table A. The inbound DST processing 82 receives read slice responses 368 (e.g., read slice responses 1-n) from the set of DS units 1-n. The inbound DST processing 82 disperse storage error decodes at least a decode threshold number of encoded table slices from the read slice responses 368 to reproduce the data object storage tracking table A.

Having recovered the data object storage tracking table A, the inbound DST processing 82 identifies one or more visible regions (e.g., data region 1) from the data object storage tracking table A. The inbound DST processing 82 identifies DS addresses associated with one or more data segments of data region 1 and issues further read slice requests 366 based on the DS addresses. The inbound DST processing 82 receives read slice responses 368 that includes one or more sets of encoded data slices (e.g., data slice A_1_1_1 through A_1_1_n for data segment 1, etc.). The inbound DST processing 82 disperse storage error decodes the one or more sets of encoded data slices to reproduce data region 1. Alternatively, or in addition to, the inbound DST processing 82 identifies the open write transaction 2 from the data object storage tracking table A and determines to subsequently recover another data region (e.g., data region 2) when the open write transaction 2 has completed. The method of operation to read the very large data object is discussed in greater detail with reference to FIG. 41.

FIG. 40I illustrates a continuation of the example of storing data. The outbound DST processing 80 of a DST client module 1 divides data region 2 into data segments of the second data region. The outbound DST processing 80 disperse storage error encodes the data segments of the second data region to produce second sets of encoded data slices (e.g., data slices A_2_1_1 through A_2_1_n for a first data segment of the second data region). The outbound DST processing 80 sends write slice requests 362 (e.g., DSN write requests) regarding storing the second sets of encoded data slices to the set of DS units 1-n. The outbound DST processing 80 updates the data object storage tracking table A to associate data region 2 with the open write transaction 2. The outbound DST processing 80 may store the updated data object storage tracking table A in the set of DS units 1-n (e.g., as an updated set of encoded table slices).

FIG. 40J illustrates a continuation of the example of storing data. When the outbound DST processing 80 of a DST client module 1 receives at least a write threshold number of write slice responses 364 (e.g., second write responses) for each of the second sets of encoded data slices, the outbound DST processing module 80 updates the data object storage tracking table A to indicate that the second data region is available by associating the identifier for data region 2 with the visible regions field 354 such that both data regions 1 and 2 are indicated as visible regions. The outbound DST processing 80 further updates the data object storing tracking table A to exclude open write transactions when the storing of the very large data object A has been completed. The outbound DST processing 80 dispersed storage error encodes the updated data object storage tracking table A to produce an updated set of encoded table slices A_1 through A_n and issues write table slice requests 360 to the set of DS units 1-n, where the write table slice requests 360 includes the updated set of encoded data slices.

When the write request is for editing the very large data object A, the outbound DST processing 80 identifies one data region as being edited based on the write request. The outbound DST processing 80 updates the data object storage tracking table A to indicate that the one data region is unavailable (e.g., an open write transaction, an open delete transaction). The outbound DST processing 80 disperse storage error encodes one or more edited data segments of a plurality of data segments of the one data region to produce one or more sets of edited encoded data slices. The outbound DST processing 80 sends updated DSN write requests 362 regarding storing the one or more sets of edited encoded data slices to the set of DS units 1-n. When at least a write threshold number of write responses 364 is received for each of the one or more sets of edited encoded data slices, the outbound DST processing 80 updates the data object storage tracking table A to indicate that the one data region is available. The method of operation to write the very large data object is discussed in greater detail with reference to FIG. 42.

FIG. 41 is a flowchart illustrating an example of reading a very large data object. The method begins at step 370 where a processing module (e.g., of a dispersed storage (DS) processing module of a computing device of a dispersed storage network (DSN)) receives a read data object request that includes an object identifier (ID). The request may also include an offset, where the offset indicates a position within the data object for initiating retrieval. The method continues at step 372 where the processing module identifies a dispersed storage (DS) address associated with the object ID. The identifying includes at least one of a directory lookup using the object ID, an index lookup using the object ID, and extracting the DS address from the read data object request. The method continues at step 374 where the processing module identifies a starting offset within a data object of the read data object request. The identifying includes at least one of extracting the starting offset from the read data object request and establishing a zero offset when not receiving the offset.

The method continues at step 376 where the processing module retrieves a region header object (e.g., a data object storage tracking table associated with the very large data object) from a DSN memory using the DS address. The retrieving includes generating a set of header slice names (e.g., table slice names) using the DS address, generating a set of read slice requests that includes the set of header slice names, outputting the set of read slice requests to the DSN memory, receiving at least a decode threshold number of header slices, and decoding the at least a decode threshold number of header slices to reproduce the region header object.

The method continues at step 378 where the processing module identifies a starting region of one or more visible regions of the region header object based on the starting offset. For example, the processing module adds region sizes of the one or more visible regions until a starting region is identified that does not exceed the starting offset. The method continues at step 380 where the processing module identifies a starting segment of the starting region based on the starting offset. The identifying includes a series of steps. A first step includes dividing a difference between the starting offset and the starting region by a segment size of the region to produce a segment number. A second step includes adding the segment number to a DS address of the region to produce a DS address of the starting segment.

The method continues at step 382 where the processing module retrieves the starting segment and any other subsequent segments of the starting region from the DSN memory. The retrieving includes a series of steps. A first step includes generating a set of retrieve slice requests for the starting segment using the DS address of the starting segment. A second step includes outputting the set of retrieve slice requests to the DSN memory. A third step includes determining a remaining number of data segments of the starting region based on segment size, region size, and the segment number of the starting segment. A fourth step includes, for each remaining data segment, generating another set of retrieve slice requests using the DS address of the starting segment and a segment number of the remaining data segment. A fifth step includes outputting the other set of retrieve slice requests to the DSN memory. A sixth step includes receiving at least a decode threshold number of encoded data slices for each of the starting data segment and the remaining data segments. A seventh step includes decoding the at least a decode threshold number of encoded data slices for each of the starting data segment and the remaining data segments using the dispersed storage error coding function to reproduce the starting data segment and the remaining data segments.

The method continues at step 384 where the processing module retrieves all segments of any subsequent visible regions to the starting region from the DSN memory. For each region entry of the one or more visible regions subsequent to the starting region, the processing module identifies a DS address of the region, determines a number of data segments based on a region size and a segment size, generates a set of slice names for each data segment (e.g., sequentially incrementing a data segment ID for each sequential data segment), generates retrieve slice requests for each set of slice names, outputs the retrieve slice requests, receives at least a decode threshold number of encoded data slices for each data segment, and decodes the at least a decode threshold number of encoded data slices for each data segment to reproduce each data segment of the region.

The method continues at step 386 where the processing module compiles retrieved data segments to reproduce the data object. The compiling includes aggregating all retrieve segments in order to reproduce the data. In addition, the processing module may include DS addresses of each region. The method continues at step 388 where the processing module outputs the data object to a requesting entity.

FIG. 42 is a flowchart illustrating an example of writing a data object. The method begins at step 390 where a processing module (e.g., of a dispersed storage processing module of a computing device of a dispersed storage network (DSN)) receives a write request regarding a very large data object. The method continues at step 392 where the processing module determines whether the write request is an initial write request for the very large data object or a write request for editing the very large data object. As examples of editing the very large data object, the editing may include revising one or more data regions of the very large data object or deleting the one or more of the data regions. As a specific example, the processing module retrieves an indication from the write request (e.g., a flag denoting a new write request). As another specific example, the processing module searches for a data object storage tracking table associated with the very large data object and, when it is not found, indicates that the write request is the initial write request. The method branches to step 404 when the write request is the initial write request. The method continues to step 394 when the write request is the write request for editing.

When the write request is for editing the very large data object, the method continues at step 394 where the processing module identifies one of the data regions being edited based on the write request (e.g., an offset identifier, a region identifier). The method continues at step 396 where the processing module updates the data object storage tracking table to indicate that the one data region is unavailable. As a specific example, the processing module retrieves at least a decode threshold number of encoded table slices of a set of encoded table slices from storage units of the DSN. Having retrieved the slices, the processing module decodes the at least a decode threshold number of encoded table slices to recapture the data object storage tracking table. Next, the processing module updates the data object storage tracking table to produce an updated data object storage tracking table (e.g., indicating that the one data region is unavailable). Having updated the data object storage tracking table, the processing module dispersed storage error encodes the updated data object storage tracking table to produce a set of updated encoded table slices. Next, the processing module writes the set of updated encoded table slices to the at least some of the storage units for storage therein.

The method continues at step 398 where the processing module disperse storage error encodes one or more edited data segments of data segments of the one data region to produce one or more sets of edited encoded data slices. The method continues at step 400 where the processing module sends updated DSN write requests regarding storing the one or more sets of edited encoded data slices to the storage units. When at least a write threshold number of write responses is received for each of the one or more sets of edited encoded data slices, the method continues at step 402 where the processing module updates the data object storage tracking table to indicate that the one data region is available.

When the write request is the initial write request, the method continues at step 404 where the processing module divides the very large data object into data regions. The method continues at step 406 where the processing module generates the data object storage tracking table that includes a section for identifying, if any, one or more data regions of the data regions that are available for retrieval and a section for identifying, if any, one or more other data regions that are unavailable for retrieval. The processing module disperse storage error encodes the data object storage tracking table to produce the set of encoded table slices and writes the set of encoded table slices to at least some of the storage units.

For a first data region, the method continues at step 408 where the processing module divides the first data region into data segments. The method continues at step 410 where the processing module generates a mapping of the data regions for the very large data object and stores the mapping in at least one of a local memory of the computing device and as a set of encoded mapping slices in at least some of the storage units. The method continues at step 412 where the processing module dispersed storage error encodes the data segments to produce sets of encoded data slices. The method continues at step 414 where the processing module sends DSN write requests regarding storing the sets of encoded data slices to the storage units of the DSN. When at least a write threshold number of write responses is received for each of the sets of encoded data slices, the method continues at step 416 where the processing module updates updating the data object storage tracking table to indicate that the first data region is available for retrieval.

Alternatively, or in addition to, the method continues at step 418 where the processing module stores a second data region. As a specific example, the processing module divides the second data region into data segments of the second data region and disperse storage error encodes the data segments of the second data region to produce second sets of encoded data slices. Next, the processing module sends second DSN write requests regarding storing the second sets of encoded data slices to the storage units. When at least a write threshold number of second write responses are received for each of the second sets of encoded data slices, the processing module updates the data object storage tracking table to indicate that the second data region is available.

Alternatively, the processing module writes one or more data regions as part of a common transaction. As a specific example, the processing module generates a transaction number for writing the one or more data regions to the storage units. When the transaction number includes at least two data regions, for a first one of the at least two data regions, the processing module divides the first one of the at least two data regions into data segments of the first one of the at least two data regions and disperse storage error encodes the data segments of the first one of the at least two data regions to produce first sets of encoded data slices. For a second one of the at least two data regions, the processing module divides the second one of the at least two data regions into data segments of the second one of the at least two data regions and disperse storage error encodes the data segments of the second one of the at least two data regions to produce second sets of encoded data slices. Next, the processing module sends DSN write requests, which include the transaction number, regarding storing the first and second sets of encoded data slices to the storage units. When at least a write threshold number of write responses is received for each of the first and second sets of encoded data slices, the processing module updates the data object storage tracking table to indicate that the first one and the second one of the at least two data regions are available.

FIG. 43 is a flowchart illustrating an example of deleting a data object, which includes similar steps to FIG. 41. The method begins at step 420 where a processing module (e.g., of a dispersed storage (DS) processing module of a computing device of a dispersed storage network (DSN)) receives a delete data object request that includes an object identifier (ID) of a very large data object stored in a dispersed storage network (DSN) memory. The method continues with steps 372 and 376 of FIG. 41 where the processing module identifies a DS address associated with the object ID and retrieves a region header object from the DSN memory using the DS address.

The method continues at step 422 where the processing module updates the region header object to include an open delete transaction. The updating includes a series of steps. A first step includes generating a transaction ID for the delete operation and generating an open delete transaction section to include the transaction ID. The region header object (e.g., data object storage tracking table) is generated in accordance with a structure of the region header object depicted in FIG. 40B. A second step includes encoding the region header object using a dispersed storage error coding function to produce a set of header slices. A third step includes generating a set of header slice names associated with the set of header slices using the DS address of the region header object. A fourth step includes generating a set of write slice requests that includes the set of header slices, the set of header slice names, and the transaction ID. A fifth step includes outputting the set of write slice requests to the DSN memory. A six step includes receiving write slice responses from the DSN memory. A seven step includes generating and outputting a set of commit requests that includes the transaction ID to the DSN memory when a write threshold number of favorable write slice responses (e.g., write succeeded without error) has been received.

The method continues at step 424 where the processing module updates the region header object by transferring each region entry of one or more region entries of a visible regions section to the open delete transaction section of the transaction ID. The method continues at step 426 where the processing module stores the updated region header object in the DSN memory. Subsequent read access to the one or more regions is now prohibited.

For each region entry associated with the open delete transaction section of the transaction ID, the method continues at step 428 where the processing module facilitates deleting the region from the DSN memory. The facilitating may include activating a cleanup task to reclaim available memory capacity of the DSN memory by deleting encoded data slices associated with the region. The deleting of the region includes a series of steps. A first step includes generating a plurality of sets of delete slice requests that includes the transaction ID and a plurality of sets of data slice names based on a DS address of the region (e.g., extracted from the region entry), a region size, and a segment size. For example, the region size is divided by the segment size to produce a number of segments. For each segment of the number of segments, a set of data slice names is generated based on the DS address of the region and incrementing a data segment field entry sequentially for each data segment. A second step includes outputting the plurality of sets of delete slice requests to the DSN memory. A third step includes receiving delete slice responses from the DSN memory. When a write threshold number of favorable delete slice responses has been received for each set of encoded data slices, a fourth step includes generating and outputting a plurality of sets of commit delete requests to the DSN memory. A fifth step includes indicating that the region is deleted when receiving a write threshold number of commit responses for each set of encoded data slices.

For each deleted region, the method continues at step 430 where the processing module updates the region header object in the DSN memory to disassociate the region with the open delete transaction. The updating includes deleting the corresponding region entry of the deleted region from the open delete transaction section of the region header object. The updating further includes storing the updated region header object in the DSN memory.

When each region of the open delete transaction section of the transaction ID has been deleted (e.g., no region entries are associated with the open delete transaction section), the method continues at step 432 where the processing module determines whether any other regions are associated with one or more of the visible regions section and an open write transaction section. The determining includes indicating no other regions are associated when there are no region entries in the visible regions section and no region entries in the open write transaction section.

When no other regions are associated with one or more of the visible regions section and the open write transactions section, the method continues at step 434 where the processing module facilitates deleting the region header object. The deleting of the region header object includes a series of steps. A first step includes identifying a current revision number of the region header object (e.g., retrieve from a local memory). A second step includes generating a set of checked delete slice requests that includes the current revision number and a set of header slice names based on the DS address of the region header object. A third step includes outputting the set of checked delete slice requests to the DSN memory. A fourth step includes receiving checked delete slice responses from the DSN memory. When a write threshold number of favorable checked delete slice responses have been received from the DSN memory, a fifth step includes generating and outputting a set of commit delete requests to the DSN memory. Alternatively, a set of checked write slice requests are generated and outputted to the DSN memory, checked write slice responses are received, and a set of delete slice requests are generating and outputted to the DSN memory when at least a write threshold number of favorable checked write slice responses are received.

FIG. 44 is a flowchart illustrating an example of overwriting a data object, which includes similar steps to FIGS. 41 and 43. The method begins at step 436 where a processing module (e.g., of a dispersed storage (DS) processing module of a computing device of a dispersed storage network (DSN)) receives a write data object request (e.g., an overwrite operation request) that includes at least a portion of a very large data object for storage and an object identifier associated with a stored very large data object stored in a dispersed storage network (DSN) memory. The method continues with steps 372 and 376 of FIG. 41 where the processing module identifies a DS address associated with the object identifier and retrieves a region header object from the DSN memory using the DS address.

The method continues at step 438 where the processing module updates the region header object to include a new open write transaction with no regions entries. For example, the processing module generates a transaction ID associated with the write request and generates an open write transaction section to include the transaction ID. The method continues at step 440 where the processing module stores the updated region header object in the DSN memory. For example, the processing module encodes the updated region header object using a dispersed storage error coding function to produce a set of header slices, generates and outputs a set of write slice requests that includes the transaction ID, the set of header slices, and a set of header slice names based on the DS address of the region header object, and generates and outputs a set of commit write requests that includes the transaction ID when a write threshold number of favorable write slice responses has been received from the DSN memory.

The method continues at step 442 where the processing module stores a first region of the data object in the DSN memory. The method continues at step 444 where the processing module updates the region header object in the DSN memory. The method continues at step 446 where the processing module stores any subsequent regions of the data object in the DSN memory. The method continues at step 448 where the processing module updates the region header object in the DSN memory for subsequent regions.

When each segment of each region has been committed, the method continues at step 450 where the processing module updates the region header object to include an open delete transaction. The updating includes generating a second transaction ID to be associated with a delete operation of the previously stored data. The method continues with step 424 of FIG. 43 where the processing module updates the region header object by transferring region entries of visible regions to the open delete transaction section.

The method continues at step 452 where the processing module updates the region header object by transferring region entries of the new open write transaction section to the visible regions section. Such transferring establishes the received data object as the current revision of the data object. The method continues at step 454 where the processing module stores the updated region header object in the DSN memory such that subsequent read operations access the received data object. The method continues with steps 428 and 430 of FIG. 43 where, for each region associated with the open delete transaction, the processing module facilitates deleting the region from the DSN memory and for each deleted region, the processing module updates the region header object in the DSN memory to disassociate the region with the open delete transaction.

FIG. 45 is a flowchart illustrating an example of resulting storage conflicts. The method begins at step 456 where a processing module (e.g., of a dispersed storage (DS) processing module of a computing device of a dispersed storage network (DSN)) determines to perform a function on a region-based object of a dispersed storage network (DSN) memory. The function includes at least one of a write, a read, a delete, an overwrite, a list, and any other function associated with a DSN. The method continues at step 458 where the processing module identifies a previous revision indicator of a region header object stored in the DSN memory. The identifying includes at least one of retrieving from a local memory (e.g., of a previously performed function), initiating a query, a lookup, and receiving the previous revision indicator.

The method continues at step 460 where the processing module generates an updated region header object based on the function. The generating includes at least one of adding a transaction identifier (ID), adding a region entry, transferring a region entry, and deleting a region entry. For example, the processing module adds a region entry in an open write transaction section of the region header object when the function includes a write operation. The method continues at step 462 where the processing module identifies a DS address associated with storage of the region header object. The identifying may be based on one or more of accessing a directory, accessing an index, a lookup using an object ID of the function, retrieving from a local memory, and receiving.

The method continues at step 464 where the processing module encodes the updated region header object to produce a set of header slices using a dispersed storage error coding function. The method continues at step 466 where the processing module generates a set of checked write slice requests based on the DS address to include the set of header slices, the previous revision indicator, and a new revision indicator. The generating includes generating a set of header slice names based on the DS address and generating the new revision indicator by incrementing the previous revision indicator by one. The method continues at step 468 where the processing module outputs the set of checked write slice requests to the DSN memory. The method continues at step 470 where the processing module receives checked write responses from the DSN memory.

The method continues at step 472 where the processing module determines whether the received checked write responses are favorable. The processing module indicates that the received checked write responses are favorable when at least a write threshold number of the received checked write responses indicate no conflict errors. The method branches to step 476 when the received checked write responses are unfavorable. The method continues to step 474 when the received checked write responses are favorable. The method continues at step 474 where the processing module updates the previous revision indicator with the new revision indicator to provide synchronization for a subsequent function.

The method continues at step 476 where the processing module retrieves the region header object from the DSN memory when the received checked write responses are unfavorable. For example, the processing module generates a set of read slice requests based on the DS address (e.g., generates a set of header slice names using the DS address), receives at least a decode threshold number of read slice responses, and decodes the at least a decode threshold number of read slice responses to produce the region header object.

The method continues at step 478 where the processing module updates the previous revision indicator with a revision indicator of the region header object. The updating includes extracting the revision indicator of the region header object from the read slice responses. The method continues at step 480 where the processing module facilitates performing the function using the region header object. The facilitating may include applying a backoff algorithm (e.g., a randomized exponential backoff process) to reduce a probability of further conflicts. The method loops back, in accordance with the backoff algorithm, to step 460.

FIG. 46A is a schematic block diagram of an embodiment of a dispersed storage system that includes a data access module 482 and a set of data storage modules 484. The set of data storage modules 484 includes at least a pillar width number of data storage modules 484 where the pillar width number is in accordance with dispersed storage error coding parameters of a dispersed storage error coding function. The data access module 482 includes a dispersed storage (DS) processing module 486 and a transceiver (TR) 488. Each data storage module 484 of the set of data storage modules 484 includes the transceiver 488 and a DS unit 490. The DS unit 490 may be implemented with one or more of a memory, a distributed storage and task execution unit, and a storage server. The transceiver 488 functions to transmit and receive wireless signals 494 via a dynamic communication path medium 496 to provide a communication path between the data access module 482 and one or more data storage modules 484. The wireless signals 494 may be generated by the transceiver 488 in accordance with a wireless industry-standard.

Wireless signals 494 are known to be of practical use between two transceivers 488 when the two transceivers 488 are within a proximal location of each other. The data storage modules 484 are geographically dispersed such that simultaneous wireless signals 494 between the data access module 482 and one or more data storage modules 484 limits a number of simultaneous communication paths to a value that is less than a decode threshold number associated with the dispersed storage error coding parameters. For example, the data access module 482 communicates wireless signals simultaneously to at maximum two of the data storage modules 484 when a pillar width is five and a decode threshold is three. As such, accessing a decode threshold number of encoded data slices stored in at least some DS units 490 of the set of data storage modules 484 is not possible for a given (e.g., at any instance) geographic relationship between the data access module 482 and the set of data storage modules 484.

The geographic relationship between the data access module 482 and the set of data storage modules 484 may change from time to time based on the dynamic communication path medium 496. In a first instance, the data access module 482 is moving relative to fixed space and as such the data access module 482 is moving relative to the set of data storage modules 484. In a second instance, the set of data storage modules 484 is moving relative to fixed space and as such the data access module 482 is moving relative to the set of data storage modules 484. In a third instance, both the data access module 482 and the set of data storage modules 484 are moving relative to fixed space and as such the data access module 482 is moving relative to the set of data storage modules 484.

When the data access module 482 is moving relative to the set of data storage modules 484, one or more different communication paths may be formed from time to time to enable the wireless signals 494 to carry communications between the data access module 482 and different data access modules 484 of the set of data access modules. For example, the data access module 482 may move relative to the set of data storage modules 484 such that a communication path is available between the data access module 482 and a first data storage module 484 during a first time period, a second communication path is available between the data access module 482 and a second data storage module 484 during a second time period, and a third communication path is available between the data access module 482 and a third data storage module 484 during a third time period such that over the course of the first, the second, and the third time period the data access module 482 can access a decode threshold number of data storage modules 484.

Access of data may include a read access scenario and a write access scenario. The dynamic communication path medium 496 may be different for a read access scenario and a write access scenario. For example, when retrieving a data segment from the set of data storage modules 484, the data access module 482 establishes wireless signals 494 with at least three of the data storage modules 484 to retrieve three corresponding encoded data slices 492 when the decode threshold is three. As another example, when storing a data segment to the set of data storage modules 484, the data access module 482 establishes wireless signals 494 with at least four of the data storage modules 484 to store for corresponding encoded data slices 492 when the write threshold is four. The method of operation of the system is discussed in greater detail with reference to FIG. 46B.

FIG. 46B is a flowchart illustrating an example of storing data. The method begins at step 500 where a processing module (e.g., of a dispersed storage (DS) processing module) encodes data using a dispersed storage error coding function to produce a plurality of sets of encoded data slices in accordance with dispersal parameters. For each pillar of each set of encoded data slices of the plurality of sets of encoded data slices, the method continues at step 502 where the processing module groups the encoded data slice that is associated with the pillar to produce a slice grouping of a pillar width number of slice groupings. Each set of encoded data slices includes a pillar width number of pillars of slices.

For each slice grouping, the method continues at step 504 where the processing module generates a write slice request that includes the slice grouping. The generating includes generating slice names for each associated encoded data slice of the slice grouping. The method continues at step 506 where the processing module detects a favorable communication path to at least one data storage module of a set of data storage modules. The detecting includes at least one of receiving a message, initiating a query, detecting a wireless signal, obtaining a communication path schedule, and predicting an availability time window for the communication path.

For each favorable communication path, the method continues at step 508 where the processing module outputs, via the favorable to communication path, an associated write slice request to a corresponding data storage module. For example, the processing module facilitates generating wireless signals based on the write slice requests and outputting the wireless signals to a transceiver associated with the corresponding data storage module.

The method continues at step 510 where the processing module receives, via the favorable communication path, a write slice response. The receiving includes facilitating the receiving of wireless signals and facilitating decoding the wireless signals to produce the write slice response. When receiving an indication of a favorable number of write slice responses (e.g., write succeeded for a write threshold number) for each set of encoded data slices of the plurality of sets of encoded data slices, the method continues at step 512 where the processing module indicates successful storage of the data. Alternatively, the processing module may facilitate resending, via the favorable communication path, the associated write slice request to the corresponding data storage module when not receiving the indication of the favorable number of write slice responses and when the detection of the favorable communication path indicates that the communication path is still available. Further alternatively, when detection of the favorable communication path indicates that the communication path is not available, the processing module may identify another data storage module to attempt to provide a favorable communication path. The identifying may include selecting the other data storage module in accordance with one or more of a schedule, a geographic location of the other data storage module, and a geographic proximity indicator to the other data storage module. The identifying may further include facilitating altering of a physical location or physical movement of at least one of the other data storage module and a data access module associated with the processing module.

FIG. 47A is a schematic block diagram of another embodiment of a dispersed storage system that includes a dispersed storage (DS) processing module 486 and a set of DS units 490. The set of DS units 490 includes at least a pillar width number of DS units 490 where the pillar width number is in accordance with dispersed storage error coding parameters of a dispersed storage error coding function. For example, the set of DS units 490 includes five DS units 490 when the pillar width is five.

The system functions to store data as a set of encoded data slices in the set of DS units 490. The storing includes a series of steps. A first step includes encoding the data (e.g., a data segment) using the dispersed storage error coding function in accordance with the dispersed storage error coding parameters to produce a set of encoded data slices. A second step includes generating a set of write slice requests 514 that includes the set of encoded data slices and a revision number. A third step includes outputting the set of write slice requests 514 to the set of DS units 490. A fourth step includes receiving write slice responses 516 from at least some of the set of DS units 490 within a time period. A write slice response 516 includes a status code indicating whether the write succeeded or if a conflict error exists. The DS processing module 486 determines that the write slice response 516 is favorable when the status code indicates that the write succeeded. A fifth step includes determining whether a write threshold number of favorable write responses 516 has been received within the time period.

When the write threshold number of favorable write responses 516 has not been received within the time period, a sixth storing step includes determining whether to wait for another time period or to immediately initiate a retry sequence. The determining may be based on one or more of a retry indicator, a query, a lookup, a response time history record, a number of non-responses, a number of favorable responses, and a number of unfavorable responses. For example, the DS processing module 486 determines to wait when receiving three favorable write responses 516 from a first three DS units 490, one conflict response 518 (e.g., an unfavorable write response) from a fourth DS unit 490, and a non-response (e.g., slow response 520) from a fifth DS unit 490 when the write threshold is four and a response time history record indicates that the fifth DS unit 490 typically responds during a second time period. As another example, the DS processing module 486 determines to retry immediately when receiving three favorable write responses 516 from the first three DS units 490, one unfavorable conflict response 518 from the fourth DS unit 490, and a slow response 520, or non-response, from the fifth DS unit 490 when the write threshold is four and a retry indicator indicates to always retry immediately when resolution of an unfavorable write response may be possible due to a conflict.

When determining to retry, a seventh step includes determining whether to modify the set of write slice requests 514 to include a different revision number. The determining may be based on one or more of an error indicator of the write responses 516, a write type indicator, a lookup, and a predetermination. For example, the DS processing module 486 determines to modify the set of write slice requests 514 to include the different revision number when one or more write responses 516 includes an unfavorable conflict response 518 due to a transaction conflict and resolution of at least one transaction conflict may produce a write threshold number of favorable write responses during a retry sequence. When determining to modify the set of write slice requests, an eighth step includes generating a modified set of write slice requests 514 that includes the different revision number. The generating includes generating the different revision number to include generating and outputting a set of read slice requests to the set of DS units 490, receiving a set of read slice responses that includes a current revision number, and incrementing the current revision number by one to produce the different revision number. A ninth step includes outputting the modified set of write slice requests 514 to the set of DS units 490. Further steps are performed as discussed above to analyze subsequent write slice responses 516 and determine next steps.

FIG. 47B is a flowchart illustrating another example of storing data. The method begins at step 522 where a processing module (e.g., of a dispersed storage (DS) processing module of a computing device of a dispersed storage network) generates a set of write slice requests that includes a set of encoded data slices and a revision number. The generating may include generating the revision number based on a previous revision number. The method continues at step 524 where the processing module outputs the set of write slice requests to a set of DS units. The method continues at step 526 where the processing module receives one or more write slice responses within a time period.

The method continues at step 530 where the processing module determines whether the one or more write slice responses includes less than a write threshold number of favorable write slice responses. When the one or more write slice responses includes less than a write threshold number of favorable write slice responses, the method continues at step 532 where the processing module determines whether to retry immediately. When retrying immediately, the method continues at step 534 where the processing module determines whether to modify the write slice requests. When modifying the write slice requests, the method continues at step 536 where the processing module updates the write slice requests to produce a set of modified write slice requests that includes a different revision number. The method continues at step 540 where the processing module outputs the set of modified write slice requests to the set of DS units. The method loops back to step 526 where the processing module receives one or more write slice responses within another time period.

As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. Such an industry-accepted tolerance ranges from less than one percent to fifty percent and corresponds to, but is not limited to, component values, integrated circuit process variations, temperature variations, rise and fall times, and/or thermal noise. Such relativity between items ranges from a difference of a few percent to magnitude differences. As may also be used herein, the term(s) “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”. As may even further be used herein, the term “operable to” or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item. As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., provides a desired relationship. For example, when the desired relationship is that signal 1 has a greater magnitude than signal 2, a favorable comparison may be achieved when the magnitude of signal 1 is greater than that of signal 2 or when the magnitude of signal 2 is less than that of signal 1.

As may also be used herein, the terms “processing module”, “processing circuit”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claimed invention. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality. To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claimed invention. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof.

The present invention may have also been described, at least in part, in terms of one or more embodiments. An embodiment of the present invention is used herein to illustrate the present invention, an aspect thereof, a feature thereof, a concept thereof, and/or an example thereof. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process that embodies the present invention may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from figure to figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones.

While the transistors in the above described figure(s) is/are shown as field effect transistors (FETs), as one of ordinary skill in the art will appreciate, the transistors may be implemented using any type of transistor structure including, but not limited to, bipolar, metal oxide semiconductor field effect transistors (MOSFET), N-well transistors, P-well transistors, enhancement mode, depletion mode, and zero voltage threshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodiments of the present invention. A module includes a processing module, a functional block, hardware, and/or software stored on memory for performing one or more functions as may be described herein. Note that, if the module is implemented via hardware, the hardware may operate independently and/or in conjunction software and/or firmware. As used herein, a module may contain one or more sub-modules, each of which may be one or more modules.

While particular combinations of various functions and features of the present invention have been expressly described herein, other combinations of these features and functions are likewise possible. The present invention is not limited by the particular examples disclosed herein and expressly incorporates these other combinations. 

What is claimed is:
 1. A method for execution by one or more processing modules of one or more computing devices of a dispersed storage network (DSN), the method comprises: receiving a write request regarding a very large data object; determining whether the write request is an initial write request for the very large data object or a subsequent write request for editing the very large data object; and when the write request is the initial write request: dividing the very large data object into a plurality of data regions; generating a data object storage tracking table that includes a section for identifying, if any, one or more data regions of the plurality of data regions that are available for retrieval and another section for identifying, if any, one or more other data regions of the plurality of data regions that are unavailable for retrieval; and for a first data region of the plurality of data regions: dividing the first data region of the plurality of data regions into a plurality of data segments; disperse storage error encoding the plurality of data segments to produce a plurality of sets of encoded data slices; sending DSN write requests regarding storing the plurality of sets of encoded data slices to storage units of the DSN; and when at least a write threshold number of write responses is received for each of the plurality of sets of encoded data slices, updating the data object storage tracking table to indicate that the first data region is available for retrieval.
 2. The method of claim 1, wherein the determining whether the write request is the initial write request for the very large data object or the subsequent write request for editing the very large data object comprises at least one of: retrieving an indication from the write request; and searching for the data object storage tracking table and, when the data object storage tracking table is not found, indicating that the write request is the initial write request.
 3. The method of claim 1 further comprises: for a second data region of the plurality of data regions: dividing the second data region of the plurality of data regions into a plurality of data segments of the second data region; disperse storage error encoding the plurality of data segments of the second data region to produce a second plurality of sets of encoded data slices; sending second DSN write requests regarding storing the second plurality of sets of encoded data slices to the storage units; and when at least a write threshold number of second write responses is received for each of the second plurality of sets of encoded data slices, updating the data object storage tracking table to indicate that the second data region is available.
 4. The method of claim 1 further comprises: generating a transaction number for writing the one or more data regions of the plurality of data regions to the storage units; and when the transaction number includes at least two data regions of the plurality of data regions: for a first one of the at least two data regions: dividing the first one of the at least two data regions into the plurality of data segments of the first one of the at least two data regions; and disperse storage error encoding the plurality of data segments of the first one of the at least two data regions to produce a first plurality of sets of encoded data slices; for a second one of the at least two data regions: dividing the second one of the at least two data regions into a plurality of data segments of the second one of the at least two data regions; and disperse storage error encoding the plurality of data segments of the second one of the at least two data regions to produce a second plurality of sets of encoded data slices; sending the DSN write requests, which include the transaction number, regarding storing the first and second plurality of sets of encoded data slices to the storage units; and when the at least a write threshold number of write responses is received for each of the first and second plurality of sets of encoded data slices, updating the data object storage tracking table to indicate that the first one and the second one of the at least two data regions are available.
 5. The method of claim 1 further comprises: when the write request is for the editing the very large data object: identifying one of the plurality of data regions being edited based on the write request; updating the data object storage tracking table to indicate that the one of the plurality of data regions is unavailable; disperse storage error encoding one or more edited data segments of a plurality of data segments of the one of the plurality of data regions to produce one or more sets of edited encoded data slices; sending updated DSN write requests regarding storing the one or more sets of edited encoded data slices to the storage units; and when the at least a write threshold number of write responses is received for each of the one or more sets of edited encoded data slices, updating the data object storage tracking table to indicate that the one of the plurality of data regions is available.
 6. The method of claim 1 further comprises: generating a mapping of the plurality of data regions for the very large data object; and storing the mapping in at least one of: local memory of at least one computing device of the one or more computing devices and as a set of encoded mapping slices in at least some of the storage units.
 7. The method of claim 1 further comprises: disperse storage error encoding the data object storage tracking table to produce a set of encoded table slices; and writing the set of encoded table slices to at least some of the storage units for storage therein.
 8. The method of claim 7 further comprises: when the data object storage tracking table is to be updated: retrieving at least a decode threshold number of encoded table slices of the set of encoded table slices; decoding the at least a decode threshold number of encoded table slices to recapture the data object storage tracking table; updating the data object storage tracking table to produce an updated data object storage tracking table; dispersed storage error encoding the updated data object storage tracking table to produce a set of updated encoded table slices; and writing the set of updated encoded table slices to the at least some of the storage units for storage therein.
 9. The method of claim 1, wherein the editing the very large data object comprises one of: revising one or more of the plurality of data regions; and deleting the one or more of the plurality of data regions. 